Bibliographic record
Abstract
Patient safety provides an important foundation for high-quality care. Research in Canada and elsewhere has identified substantial levels of harm in hospitals and other settings; these results spurred the development and spread of safety practices, along with strategies to strengthen organizational training, incident reporting and analysis and a host of resources intended to reduce the burden of harm. Yet, despite these efforts, 20 years after the publication of the Canadian Adverse Event study (Baker et al. 2004) and other studies, many leaders believe progress in patient safety has stalled (NEJM Catalyst 2023). Indeed, some recent studies indicate that the levels of harm have increased. One notable study by David Bates and colleagues (2023), building on approaches used in earlier studies, identified at least one adverse event in 23.6% of a random sample of patients in Massachusetts hospitals in 2018. Among 978 events, 22.7% were judged preventable and one-third required at least substantial intervention or prolonged recovery.
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 | 0.079 | 0.169 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.017 | 0.036 |
| Insufficient payload (model declined to judge) | 0.024 | 0.010 |
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".