The importance of cognition for improving diagnostic safety: Salerno redux?
Bibliographic record
Abstract
The oldest medical school of modern civilization, in Salerno, Italy, prioritized the study of philosophy, logic, and reasoning. We first retrace the history of how clinical reasoning and its perceived importance has evolved, culminating ultimately in the 2015 National Academies report on diagnostic error in healthcare. The report clearly emphasized the fundamental role of clinical reasoning in diagnosis, and the critical need to optimize the cognitive elements of diagnosis to prevent diagnostic errors in the future. The dual processing paradigm, envisioning both intuitive and rational pathways, is central to current understandings of clinical reasoning. The importance of knowledge, the impact of cognitive biases, the influence of context, and many other 'adjacent' factors also impact the likelihood of arriving at the correct diagnosis. Medical education needs to re-prioritize cognition over content, and teach clinical reasoning interprofessionally. Emphasizing rationality and recognizing cognitive and affective bias are key. A host of interventions have been proposed: patient engagement, second opinions, reflection, improving teamwork, and using AI are all well justified and worthy of trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".