Bibliographic record
Abstract
Routine monitoring of UK GPs' mortality rates has been recommended by the Shipman Inquiry, and is likely to be implemented soon.1-3 In this Journal, Mohammed et al 4 are to be applauded for their rigorous attempt to address the potential problems of such monitoring.2,3 In particular, they describe the application of structured investigation to practices with unexpectedly high or low mortality rates that is a potential model for any national system.5 Ultimately though, many uncertainties remain. Crucially, what mortality monitoring is intended to achieve needs to be clearly articulated, and reflected in monitoring system design. The two purposes usually identified are, first to deter or detect murderous clinicians or those dangerous through ignorance, incompetence or illness, and secondly to improve the overall quality of care.2,3,6 Whether any system can achieve the first of these is to some extent unknowable, but the Northern Ireland pilot left considerable space for the unscrupulous to avoid detection. One quarter of practices were excluded from the primary cross-sectional analysis because they did not have data for long enough due to practice mergers or splits, and participation in investigation was voluntary. Additionally, mortality rates were monitored at practice level, which reduces the ability to detect individual doctors with high mortality, and will not detect dangerous doctors working out-of-hours or as locums. However, it remains uncertain whether it is possible to create a monitoring system able to attribute mortality reliably to individual doctors under current practice and NHS …
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 | 0.046 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.023 | 0.011 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".