Bibliographic record
Abstract
Safety in health care has increasingly become a key focus of health care providers. Data on "patient outcomes" and evidence-based clinical decision-making have led to real changes in health care policy and care provision. Specialist groups such as the National Patient Safety Agency which operates the National Confidential Enquiry into Patient Outcome and Death (NCEPOD) in the United Kingdom are reliant on good information in order to identify factors that lead to poor patient care. In a recent study the NCEPOD reviewed the quality of coroners' autopsy reports on which they rely for much of their core data. The study found that just over half of the reports (52%) were considered satisfactory by the reviewers, 19% were good and 4% were excellent. However, over a quarter of autopsies were marked as poor or of an unacceptable standard. While analysing the factors associated with poor-quality autopsies, comments and recommendations were made with regard to the processes of death investigation and the degree to which the coroner's death investigation meets the needs of health care services.
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 | 0.069 | 0.274 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".