Lessons in clinical reasoning - pitfalls, myths, and pearls: a case of tarsal tunnel syndrome caused by an intraneural ganglion cyst.

OBJECTIVES: Intraneural ganglionic cysts are non-neoplastic cysts that can cause signs and symptoms of peripheral neuropathy. However, the scarcity of such cases can lead to cognitive biases. Early surgical exploration of space occupying lesions plays an important role in identification and improving the outcomes for intraneural ganglionic cysts. CASE PRESENTATION: This patient presented with loss of sensation on the right sole with tingling numbness for six months. A diagnosis of tarsal tunnel syndrome was made. Nerve conduction study revealed that the mixed nerve action potential (NAP) was absent in the right medial and lateral plantar nerves. The magnetic resonance imaging (MRI) found a cystic lesion measuring 1.4×1.8×3.8 cm as the presumed cause of the neuropathy. Surgical exploration revealed a ganglionic cyst traversing towards the flexor retinaculum with baby cysts. The latter finding came as a surprise to the treating surgeon and was confirmed to be an intraneural ganglionic cyst based on the histopathology report. CONCLUSIONS: Through integrated commentary by a case discussant and reflection by an orthopedician, this case highlights the significance of the availability heuristic, confirmation bias, and anchoring bias in a case of rare disease. Despite diagnostic delays, a medically knowledgeable patient's involvement in their own care lead to a more positive outcome. A fish-bone diagram is provided to visually demonstrate the major factors that contributed to the diagnostic delay. Finally, this case provides clinical teaching points in addition to a pitfall, myth, and pearl related to availability heuristic and the sunk cost fallacy.

Active learning of potential-energy surfaces of weakly bound complexes with regression-tree ensembles.

Several pool-based active learning (AL) algorithms were employed to model potential-energy surfaces (PESs) with a minimum number of electronic structure calculations. Theoretical and empirical results suggest that superior strategies can be obtained by sampling molecular structures corresponding to large uncertainties in their predictions while at the same time not deviating much from the true distribution of the data. To model PESs in an AL framework, we propose to use a regression version of stochastic query by forest, a hybrid method that samples points corresponding to large uncertainties while avoiding collecting too many points from sparse regions of space. The algorithm is implemented with decision trees that come with relatively small computational costs. We empirically show that this algorithm requires around half the data to converge to the same accuracy in comparison to the uncertainty-based query-by-committee algorithm. Moreover, the algorithm is fully automatic and does not require any prior knowledge of the PES. Simulations on a 6D PES of pyrrole(H2O) show that <15 000 configurations are enough to build a PES with a generalization error of 16 cm-1, whereas the final model with around 50 000 configurations has a generalization error of 11 cm-1.