Bibliographic record
Abstract
Abstract Behind heart disease and cancer, medical error is now listed as one of the leading causes of death. Of the medical errors that lead to injury and death, diagnostic failure is regarded as the most significant. Generally, the majority of diagnostic failures are attributed to the clinicians directly involved with the patient, and to a lesser extent, the system in which they work. In turn, the majority of errors made by clinicians is due to decision making failures manifested by various departures from rationality. Of all the medical environments in which patients are seen and diagnosed, the emergency department is the most challenging. It has been described as a ‘wicked’ environment where illness and disease may range from minor ailments and complaints to severe, life-threatening disorders. The Cognitive Autopsy is a novel strategy towards understanding medical error and diagnostic failure in 42 clinical cases with which the author was directly involved or became aware of at the time. Essentially, it describes a cognitive approach towards root cause analysis of medical adverse events or near misses. Whereas root cause analysis typically focuses on the observable and measurable aspects of adverse events, the cognitive autopsy attempts to identify covert cognitive processes that may have contributed to outcomes. In this clinical setting, no cognitive process is directly observable but must be inferred from the behaviour of the individual clinician. The book illustrates unequivocally that chief among these cognitive processes are cognitive biases and other flaws in decision making, rather than knowledge deficits.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.011 |
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".