Bibliographic record
Abstract
For many years safety officials and public health authorities have discouraged use of the word “accident” when it refers to injuries or the events that produce them. An accident is often understood to be unpredictable—a chance occurrence or an “act of God”—and therefore unavoidable. However, most injuries and their precipitating events are predictable and preventable.1–3 That is why the BMJ has decided to ban the word accident. In an editorial in the BMJ in 1993 Evans explained why “motor vehicle crash” is an appropriate expression but “motor vehicle accident” is not: “The word crash indicates in a simple factual way what is observed, while accident seems to suggest in addition a general explanation of why it occurred without any evidence to support such an explanation.”4 Evans also argued that “accident” is inappropriate in reference to medical errors (as in medical accidents) and that “its use in medical settings continues to mislead.”4 Eight years later “accident” continues to be misused in medical circles—and on the pages of the BMJ . An online search for “accident” in the BMJ for the period January 1996 to December 2000 indicated that it has been used in the title or …
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 | 0.013 | 0.083 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.020 | 0.020 |
| Insufficient payload (model declined to judge) | 0.063 | 0.089 |
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".