Bibliographic record
Abstract
Patient safety and medical error have become prominent issues following publication of Institute of Medicine reports in the USA. The USA, Australia, and now Canada have followed a national “medical error” studies path that uses language rejected by the interdisciplinary group of experts described previously in this column, and continues using methods considered seriously flawed as well as incomplete by noteworthy hospital epidemiologists. Preliminary review of British hospitals by similar methods also has been published. Proven and more cost‐effective surveillance methods are pertinent methods developed over the past several decades by hospital epidemiology and infection control professionals who have more experience, but this heritage has been ignored in recent patient safety juggernauts. It is time to question why retrospective physician chart review approaches remain in vogue with national bodies to enumerate adverse patient outcomes and attribute them with “medical error” when better alternatives exist.
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 | 0.032 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.017 | 0.037 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".