Bibliographic record
Abstract
After years of culture of silence, medical error and patient safety have become topics for open discussion. Public expectations of greater transparency have combined with a widening focus on systemic contributions to error. For example, the Canadian Institutes of Health Research and the Canadian Institute for Health Information have issued a call for proposals to study health-system error in Canada. At Health Canada, the Therapeutic Products Directorate now collects data on medication error as part of its monitoring of adverse drug events (www.hc-sc.gc.ca/hpb-dgps/therapeut/htmleng). Other Canadian institutions with an interest in system improvement and patient safety include the CQI Network (www.thecqinetwork.com), which offers workshops on planning and implementing quality improvement, the National Association of Pharmacy Regulatory Authorities (www.napra.org), the Canadian Society of Hospital Pharmacists (www.cshp.ca) and the Canadian Nurses Association (www .cna-nurses.ca). Halifax bioethicist Chris MacDonald has collected links to organizations, institutes, publications and reports under the title Ethical Aspects of Clinical Error and Patient Safety (www.medicalerrors.ca). In the US, the Institute of Medicine's Quality of Health Care in America Project (www.iom.edu/qhca) has produced an influential report on the scale and causes of and solutions for medical error, Crossing the Quality Chasm: A New Health System for the 21st Century (www.nap.edu/catalog/10027.html). The US Agency for Healthcare Research and Quality has information on error occurrence and preventive measures (www.ahrq.gov/errors.htm). The Institute for Safe Medication Practices in the US has been collecting data and issuing warnings and recommendations on medication error for more than 25 years (www.ismp.org). ISMP Canada, an independent nonprofit agency with close ties to its American counterpart, was created in 1999. Its Web site (www.ismp-canada.org) offers an anonymous reporting system for medication errors, as well as newsletters and a list of links to collaborating organizations.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.059 | 0.020 |
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".