Disclosing the undisclosed: are radiographers and healthcare scientists required to communicate a provisional diagnosis when asked?

Patients need to be given the relevant information to be able to give informed consent, which might require the disclosure of a provisional diagnosis. Yet, there is no duty to give information to a patient if that patient is aware that this information exists but chooses not to request it. Diagnostic radiographers and healthcare scientists are often responsible for ensuring that patients have given informed consent for the investigations they undertake, but which were requested by other clinicians. Here we examine if they have a duty to disclose a patient's provisional diagnosis made by a referring clinician if the patient asks for this information as part of the informed consent process to a diagnostic investigation. We first consider aspects of UK law, professional guidance and salient ethical principles, emphasising that while professional codes of practice highlight the need to act in the patient's best interest, they do not require giving patients information they do not require for the examination or have not requested. We then propose that diagnostic radiographers and healthcare scientists placed in such a position use a 'minimally necessary disclosure' framework. This framework fulfils their commitment to their patient and the principle of veracity, while respecting the boundaries of their professional duties. The framework ensures that enough detail is given to the patient for them to be able to give informed consent, while shouldering the diagnostic professional from making a full disclosure, which is the duty of the referring clinician.

[The lie: Hiding to tell, telling to hide]. / Le mensonge : cacher pour dire, dire pour cacher.

We all lie. Some more than others, and others still have made it a way of life in relationships. There is a fine line between the normal and the pathological. It is certainly more psychologically comfortable to side with the truth than with lies. So what is it that drives the liar to stick to his guns?

Automated Classification of Physiologic, Glaucomatous, and Glaucoma-Suspected Optic Discs Using Machine Learning.

In order to generate a machine learning algorithm (MLA) that can support ophthalmologists with the diagnosis of glaucoma, a carefully selected dataset that is based on clinically confirmed glaucoma patients as well as borderline cases (e.g., patients with suspected glaucoma) is required. The clinical annotation of datasets is usually performed at the expense of the data volume, which results in poorer algorithm performance. This study aimed to evaluate the application of an MLA for the automated classification of physiological optic discs (PODs), glaucomatous optic discs (GODs), and glaucoma-suspected optic discs (GSODs). Annotation of the data to the three groups was based on the diagnosis made in clinical practice by a glaucoma specialist. Color fundus photographs and 14 types of metadata (including visual field testing, retinal nerve fiber layer thickness, and cup-disc ratio) of 1168 eyes from 584 patients (POD = 321, GOD = 336, GSOD = 310) were used for the study. Machine learning (ML) was performed in the first step with the color fundus photographs only and in the second step with the images and metadata. Sensitivity, specificity, and accuracy of the classification of GSOD vs. GOD and POD vs. GOD were evaluated. Classification of GOD vs. GSOD and GOD vs. POD performed in the first step had AUCs of 0.84 and 0.88, respectively. By combining the images and metadata, the AUCs increased to 0.92 and 0.99, respectively. By combining images and metadata, excellent performance of the MLA can be achieved despite having only a small amount of data, thus supporting ophthalmologists with glaucoma diagnosis.

The evolution of raw data archiving and the growth of its importance in crystallography.

The hardware for data archiving has expanded capacities for digital storage enormously in the past decade or more. The IUCr evaluated the costs and benefits of this within an official working group which advised that raw data archiving would allow ground truth reproducibility in published studies. Consultations of the IUCr's Commissions ensued via a newly constituted standing advisory committee, the Committee on Data. At all stages, the IUCr financed workshops to facilitate community discussions and possible methods of raw data archiving implementation. The recent launch of the IUCrData journal's Raw Data Letters is a milestone in the implementation of raw data archiving beyond the currently published studies: it includes diffraction patterns that have not been fully interpreted, if at all. The IUCr 75th Congress in Melbourne included a workshop on raw data reuse, discussing the successes and ongoing challenges of raw data reuse. This article charts the efforts of the IUCr to facilitate discussions and plans relating to raw data archiving and reuse within the various communities of crystallography, diffraction and scattering.

More familiar, more credible? Distinguishing two types of familiarity on the truth effect using the drift-diffusion model.

Familiar information is more likely to be accepted as true. This illusory truth effect has a tremendous negative impact on misinformation intervention. Previous studies focused on the familiarity from repeated exposure in the laboratory, ignoring preexisting familiarity with real-world misinformation. Over three studies (total N = 337), we investigated the cognitive mechanisms behind the truth biases from these two familiarity sources, and whether fact-checking can curb such biased truth perceptions. Studies 1 and 2 found robust truth effects induced by two sources of familiarity but with different cognitive processes. According to the cognitive process model, repetition-induced familiarity reduced decision prudence. Preexisting familiarity instead enhanced truth-congruent evidence accumulation. Study 3 showed that pre-exposing statements with warning flags eliminated the bias to truth induced by repetition but not that from preexisting familiarity. These repeated statements with warning labels also reduced decision caution. These findings furthered the understanding of how different sources of familiarity affect truth perceptions and undermine the intervention through different cognitive processes.

How individuals perceive their partner's relationship behaviors when worrying about finances.

What role do financial worries play in close relationship functioning? In this research, we examine how financial worry - negative thoughts and feelings about finances - is associated with perceived relationship behaviors. Participants recalled how their partner acted during a recent disagreement (Study 1, N = 97 couples) or recalled the frequency of positive and negative behaviors enacted by their partner during the previous week (Study 2, N = 99 couples). Feeling more worried about finances was associated with recalling less supportive behavior from one's partner at the disagreement (Study 1) and with perceiving more negative behaviors from one's partner in the last week (Study 2). Truth and Bias Model analyses suggest that part of this link may be attributed to biased perceptions, as the link between financial worry and perceiving more negative behaviors persisted even after controlling for participants' own reported behaviors (i.e., accounting for similarity) and for their partner's own reported behaviors (i.e., accounting for accurate perceptions). In sum, financial worry is linked to how partners notice and interpret a loved one's actions within their relationship.

Establishing veritocracy: Society, truth and science.

In the west, truth is being eroded by post-modernist ideas such as alternative facts. Once truth is no longer valued it is a short route through nationalistic populism to fascism. To combat this we need to establish the idea of 'veritocracy' as a form of government. A veritocracy is a democracy in which truth is so highly valued that promising to tell the truth will become a central feature of politicians' election manifestos feeding back the idea of veritocracy deeper into national culture. A proper understanding of the nature of science can support the idea of veritocracy. This proper understanding will not repeat the mythology of post-World War II philosophy and history of science, but will begin with the much more socially cognisant revolution in our understanding of science that began in the 1960s and 1970s. Nevertheless, a 'wave three' of science studies will justify science, not as a certainty-maker for policy, but as the way to bet in developing the best understanding of the observable world. The key is that science depends on moral truth in its attempts to develop correspondence truth. Science, like the law, should be a 'check and balance' in pluralist democracies and an object lesson in how to pursue truth in decision-making.

Telling the truth to patients before hip fracture surgery.

BACKGROUND: Hip fracture repair surgery carries a certain mortality risk, yet evidence suggests that orthopedic surgeons often refrain from discussing this issue with patients prior to surgery. AIM: This study aims to examine whether orthopedic surgeons raise the issue of one-year post-surgery mortality before hip fracture repair surgery and to explore factors influencing this decision. METHOD: The study employs a cross-sectional design, administering validated digital questionnaires to 150 orthopedic surgeons. RESULTS: A minority of orthopedic surgeons reported always informing patients about the risk of mortality in the year following hip fracture surgery. The main reasons for not discussing this risk were a desire to avoid frightening patients, time constraints, and concerns about undermining patient hope. Orthopedic surgeons reported a medium-high level of perceived self-efficacy, with higher self-efficacy associated with a reduced likelihood of discussing one-year mortality risk. Conversely, older age and holding a specialist status in orthopedic surgery were associated with an increased likelihood of discussing this risk with patients. CONCLUSIONS: These findings suggest a need for interventions to address communication barriers and ensure consistent provision of essential information to patients undergoing hip fracture surgery. Additionally, they highlight the importance of considering individual factors such as self-efficacy, age, and expertise in designing strategies to improve patient-provider communication in orthopedic care settings. TRIAL REGISTRATION: The study doesn`t report the results of a health care intervention.

Nurses' experiences of reporting the medical errors of their colleagues: a qualitative study.

BACKGROUND: Medical error is a leading cause of disability and death in healthcare settings and reporting colleagues' medical errors is one of the main strategies for medical error management and an ethical responsibility of all healthcare providers, including nurses. Most studies into reporting colleagues' medical errors used quantitative designs while it seems that using qualitative designs can provide better insight in this area. PURPOSE: This study explored nurses' experiences of reporting the medical errors of their colleagues. METHODS: This qualitative study was conducted using the conventional content analysis approach. Participants were 22 hospital nurses purposively selected in 2021-2022 from different cities in Iran. Twenty-two in-depth semi-structured interviews were held for data collection. The data were analyzed via Graneheim and Lundman's conventional content analysis and trustworthiness was maintained using the criteria proposed by Guba and Lincoln. FINDINGS: The main categories of the study were burnout and intention to leave the profession and growth and development. The two subcategories of the first category were the experience of injury and the experience of violence and the two subcategories of the second category were sense of worthiness and sense of motivation. Moral distress was the most important experience of almost all participants. CONCLUSION: Nurses mostly have negative experiences in terms of reporting their colleagues' medical errors. Negative experiences can act as the barriers to report colleagues' errors while positive experiences can act as its facilitators. Improvement of the patient safety culture in healthcare settings and interpersonal relationships among healthcare providers can reduce the negative experiences and promote the positive experiences of reporting colleagues' medical errors.

Experiences and preferences of truth-telling in families of children with cancer: A phenomenological study.

Objective: The delivery of bad news is an unpleasant but necessary medical procedure. However, few studies have addressed the experiences and preferences of the families of school-aged children with cancer when they are informed of the children's condition. This study aimed to explore families of school-age children with cancer for their preferences and experiences of truth-telling. Methods: This descriptive phenomenological qualitative research was conducted using focus group interviews and semistructured interview guidelines were adopted for in-depth interviews. Fifteen families participated in the study. The data were analyzed using Colaizzi's analysis. Data were collected from August 2019 to May 2020. Results: The study identified two major themes: "caught in a dilemma" and "kind and comprehensive team support." The first major theme focused on families' experiences with cancer truth-telling. Three sub-themes emerged: (1) cultural aspects of cancer disclosure, (2) decision-making regarding informing pediatric patients about their illness, and (3) content of disclosure after weighing the pros and cons. The second major theme, which revealed families' preferences for delivering bad news, was classified into three sub-themes: (1) have integrity, (2) be realistic, and (3) be supportive. Conclusions: This study underscores the dilemma encountered by the families of children with cancer after disclosure and their inclination toward receiving comprehensive information and continuous support. Health care personnel must improve their truth-telling ability in order to better address the needs of such families and to provide continuous support throughout the truth-telling process.

Continuous distribution of cancer cells in the cell cycle unveiled by AI-segmented imaging of 37,000 HeLa FUCCI cells.

Classification of live or fixed cells based on their unlabeled microscopic images would be a powerful tool for cell biology and pathology. For such software, the first step is the generation of a ground truth database that can be used for training and testing AI classification algorithms. The Application of cells expressing fluorescent reporter proteins allows the building of ground truth datasets in a straightforward way. In this study, we present an automated imaging pipeline utilizing the Cellpose algorithm for the precise cell segmentation and measurement of fluorescent cellular intensities across multiple channels. We analyzed the cell cycle of HeLa-FUCCI cells expressing fluorescent red and green reporter proteins at various levels depending on the cell cycle state. To build the dataset, 37,000 fixed cells were automatically scanned using a standard motorized microscope, capturing phase contrast and fluorescent red/green images. The fluorescent pixel intensity of each cell was integrated to calculate the total fluorescence of cells based on cell segmentation in the phase contrast channel. It resulted in a precise intensity value for each cell in both channels. Furthermore, we conducted a comparative analysis of Cellpose 1.0 and Cellpose 2.0 in cell segmentation performance. Cellpose 2.0 demonstrated notable improvements, achieving a significantly reduced false positive rate of 2.7 % and 1.4 % false negative. The cellular fluorescence was visualized in a 2D plot (map) based on the red and green intensities of the FUCCI construct revealing the continuous distribution of cells in the cell cycle. This 2D map enables the selection and potential isolation of single cells in a specific phase. In the corresponding heatmap, two clusters appeared representing cells in the red and green states. Our pipeline allows the high-throughput and accurate measurement of cellular fluorescence providing extensive statistical information on thousands of cells with potential applications in developmental and cancer biology. Furthermore, our method can be used to build ground truth datasets automatically for training and testing AI cell classification. Our automated pipeline can be used to analyze thousands of cells within 2 h after putting the sample onto the microscope.

Ethical and moral principles for oncology healthcare workers: A brief report from a Bioethics consortium emphasizing on need for education.

The medical sub-specialty of Oncology presents diverse ethical dilemmas, often challenging cancer healthcare workers with difficult-to-handle clinical scenarios that are tough from a personal and professional perspective. Making decisions on patient care in various circumstances is a defining obligation of an oncologist and those duty-based judgments entail more than just selecting the best treatment or solution. Ethics is an essential and inseparable aspect of clinical medicine and the oncologists as well as the allied health care workers are ethically committed to helping the patient, avoiding or minimizing harm, and respecting the patient's values and choices. This review provides an overview of ethics and clinical ethics and the four main ethical principles of autonomy, beneficence, non-maleficence, and justice are stated and explained. At times there are frequently contradictions between ethical principles in patient care scenarios, especially between beneficence and autonomy. In addition, truth-telling, professionalism, empathy, and cultural competence; which are recently considered important in cancer care, are also addressed from an Indian perspective.

The Crosswise Model for Surveys on Sensitive Topics: A General Framework for Item Selection and Statistical Analysis.

When surveys contain direct questions about sensitive topics, participants may not provide their true answers. Indirect question techniques incentivize truthful answers by concealing participants' responses in various ways. The Crosswise Model aims to do this by pairing a sensitive target item with a non-sensitive baseline item, and only asking participants to indicate whether their responses to the two items are the same or different. Selection of the baseline item is crucial to guarantee participants' perceived and actual privacy and to enable reliable estimates of the sensitive trait. This research makes the following contributions. First, it describes an integrated methodology to select the baseline item, based on conceptual and statistical considerations. The resulting methodology distinguishes four statistical models. Second, it proposes novel Bayesian estimation methods to implement these models. Third, it shows that the new models introduced here improve efficiency over common applications of the Crosswise Model and may relax the required statistical assumptions. These three contributions facilitate applying the methodology in a variety of settings. An empirical application on attitudes toward LGBT issues shows the potential of the Crosswise Model. An interactive app, Python and MATLAB codes support broader adoption of the model.

Synergizing Deep Learning-Enabled Preprocessing and Human-AI Integration for Efficient Automatic Ground Truth Generation.

The progress of incorporating deep learning in the field of medical image interpretation has been greatly hindered due to the tremendous cost and time associated with generating ground truth for supervised machine learning, alongside concerns about the inconsistent quality of images acquired. Active learning offers a potential solution to these problems of expanding dataset ground truth by algorithmically choosing the most informative samples for ground truth labeling. Still, this effort incurs the costs of human labeling, which needs minimization. Furthermore, automatic labeling approaches employing active learning often exhibit overfitting tendencies while selecting samples closely aligned with the training set distribution and excluding out-of-distribution samples, which could potentially improve the model's effectiveness. We propose that the majority of out-of-distribution instances can be attributed to inconsistent cross images. Since the FDA approved the first whole-slide image system for medical diagnosis in 2017, whole-slide images have provided enriched critical information to advance the field of automated histopathology. Here, we exemplify the benefits of a novel deep learning strategy that utilizes high-resolution whole-slide microscopic images. We quantitatively assess and visually highlight the inconsistencies within the whole-slide image dataset employed in this study. Accordingly, we introduce a deep learning-based preprocessing algorithm designed to normalize unknown samples to the training set distribution, effectively mitigating the overfitting issue. Consequently, our approach significantly increases the amount of automatic region-of-interest ground truth labeling on high-resolution whole-slide images using active deep learning. We accept 92% of the automatic labels generated for our unlabeled data cohort, expanding the labeled dataset by 845%. Additionally, we demonstrate expert time savings of 96% relative to manual expert ground-truth labeling.

Reichsanzeiger-GT: An OCR ground truth dataset based on the historical newspaper "Deutscher Reichsanzeiger und Preußischer Staatsanzeiger" (German Imperial Gazette and Prussian Official Gazette) (1819-1945).

Reichsanzeiger-GT is a ground truth dataset for OCR training and evaluation based on the historical German newspaper "Deutscher Reichsanzeiger und Preußischer Staatsanzeiger" (German Imperial Gazette and Prussian Official Gazette), which was published from 1819 to 1945 and printed mostly in the typeface Fraktur (Black Letter). The dataset consists of 101 newspaper pages for the years 1820-1939, that cover a wide variety of topics, page layouts (lists, tables, and advertisements) as well as different typefaces. Using the transcription software Transkribus and the open-source OCR engine Tesseract we automatically created and manually corrected layout segmentations and transcriptions for each page, resulting in 65,563 text regions, 412 table regions, 119,429 text lines and 490,679 words. By applying transcription guidelines that preserve the printing conditions, the dataset contains language and printing specific phenomena like the historical use of glyphs like long s (s), rotunda r (ꝛ), and historical currency symbols (M, ₰) among others. The dataset is provided in two variants in PAGE XML format. The first one contains ground truth data with table regions transformed to text regions for easier processing. The second variant preserves all table regions. Researchers can reuse this dataset to train new or finetune existing text recognition or layout segmentation models. The dataset can also be used to evaluate the accuracy of existing OCR models. Using specific, community driven transcription guidelines our dataset is easily interoperable and reusable with other datasets based on the same transcription level.

The ambiguity of "true" in English, German, and Chinese.

Through a series of empirical studies involving native speakers of English, German, and Chinese, this paper reveals that the predicate "true" is inherently ambiguous in the empirical domain. Truth statements such as "It is true that Tom is at the party" seem to be ambivalent between two readings. On the first reading, the statement means "Reality is such that Tom is at the party." On the second reading, the statement means "According to what X believes, Tom is at the party." While there appear to exist some cross-cultural differences in the interpretation of the statements, the overall findings robustly indicate that "true" has multiple meanings in the realm of empirical matters.

Illusions of knowledge due to mere repetition.

Repeating information increases people's belief that the repeated information is true. This truth effect has been widely researched and is relevant for topics such as fake news and misinformation. Another effect of repetition, which is also relevant to those topics, has not been extensively studied so far: Do people believe they knew something before it was repeated? We used a standard truth effect paradigm in four pre-registered experiments (total N = 773), including a presentation and judgment phase. However, instead of "true"/"false" judgments, participants indicated whether they knew a given trivia statement before participating in the experiment. Across all experiments, participants judged repeated information as "known" more often than novel information. Participants even judged repeated false information to know it to be false. In addition, participants also generated sources of their knowledge. The inability to distinguish recent information from well-established knowledge in memory adds an explanation for the persistence and strength of repetition effects on truth. The truth effect might be so robust because people believe to know the repeatedly presented information as a matter of fact.

The effect of value on context and target recollection in memory for truth and falsity.

Memory for truth and falsity has recently been investigated from the perspective of the dual-recollection theory, showing better context and target recollection for truth than falsity. In this paper, we examine whether these memory effects obtained for true statements are similar to the value effect, whereby true statements are given higher priority in encoding. For this purpose, we implemented value-directed remembering (VDR) into the conjoint-recognition paradigm. In our first experiment, the primary goal was to verify how VDR influences the processes defined by dual-recollection theory. At study, prioritized/important items were linked to higher numerical values (e.g., 10), while unimportant ones had lower values (e.g., 1). At test, the participants' task was to recognize whether a particular sentence was important, unimportant, or new. We found that both context and target recollection were better for important items. In the second experiment, the main goal was to study the combined effects of importance and veracity on memory. In the between-subjects design, participants were monetarily rewarded for memorizing true or false sentences. The results demonstrated differences in the ability to prioritize truth over falsity. Specifically, we found a substantial increase in context recollection for prioritized true information but not for prioritized false information. Moreover, we found higher context recollection for true than false sentences in the true-prioritized condition, but not in the false-prioritized condition. These results indicated that people are able to prioritize true information better than false, and suggested that memory for truth may be a special case of the value effect.

A template for creating and sharing ground truth data in digital forensics.

Ground truth data (GTD) is used by those in the field of digital forensics (DF) for a variety of purposes including to evaluate the functionality of undocumented, new, or emerging technology and services and the digital traces left behind following their usage. Most accepted and reliable trace interpretations must be derived from an examination of relevant GTD, yet despite the importance of it to the DF community, there is little formal guidance available for supporting those who create it, to do so in a way that ensures any data is of good quality, reliable, and therefore usable. In an attempt to address this issue, this work proposes a minimum standard of documentation that must accompany the production of any GTD, particularly when it is intended for use in the process of discovering new knowledge, proposing original interpretations of a digital trace, or determining the functionality of any technology or service. A template structure is discussed and provided in Appendix S1 which sets out a minimum standard for metadata describing any GTD's production process and content. It is suggested that such an approach can support the maintenance of trust in any GTD and improve the shareability of it.

Truth for the Moment and Importance to Nursing.

In the article that follows the authors present information focused on nursing care delivery involving Neuman systems theory and improving the stress and stability. The authors then follow up with a clinical scenario which utilizes the Neuman system model as a means to prevent and ameliorate aggression and violence. The structure and evidence guided by Betty Neuman's systems model centers on managing and reducing stresses and working towards reconstitution. The thoughts contained in this article highlight some considerations regarding the importance of truth and truth for the moment for the nursing discipline.