Bibliographic record
Abstract
Extract Pat Croskerry is Professor of Emergency Medicine and in the Division of Medical Education & Continuing Professional Development, Faculty of Medicine at Dalhousie University in Halifax, Nova Scotia, Canada. In addition to his medical training, he holds a doctorate in Experimental Psychology and a Fellowship in Clinical Psychology. He has published over 90 journal articles and 40 book chapters in the area of patient safety, clinical decision making and medical education reform. Two of his papers are in the top 3 cited papers in the emergency medicine education literature. In 2006 he was appointed to the Board of the Canadian Patient Safety Institute, and in the same year received the Ruedy award from the Association of Faculties of Medicine of Canada for innovation in medical education. He has given over 500 keynote presentations at leading medical schools, hospitals, and universities around the world. He is senior editor of Patient Safety in Emergency Medicine (2009), and senior author of Diagnosis: Interpreting the Shadows (2017). He was appointed Director of the new Critical Thinking Program at Dalhousie Medical School in 2012. He is a Fellow of the Royal College of Physicians of Edinburgh. In 2014, he was appointed to the US Institute of Medicine Committee on Diagnostic Error in Medicine. He was nominated to the Canadian Association of Emergency Physicians Top Ten List of most impactful Canadian medical educators in 2016.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.550 | 0.445 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".