Bibliographic record
Abstract
A recent CMAJ lead editorial notes that failures to manage patients according to widely accepted standards of care may be more common than the medical errors that result in serious adverse events in Canadian hospitals.1 The editorial goes on to suggest that “process-of-care standards could be implemented in hospital and ambulatory practice; adherence could be monitored and the results disclosed.”1 In BC we have established many standards of care through our clinical practice guidelines and we monitor adherence to many of them through administrative data. We can easily confirm your suspicion concerning the prevalence of failures to manage patients according to accepted standards of care.2 I am dismayed that you feel that public disclosure of the results of such monitoring will push physicians to improve their scores. This approach is rooted in the culture of blame that bedevils our health system and that so often leads to selective reporting, gaming, concealment and lack of cooperation with otherwise promising quality improvement initiatives. Measurement should be for learning, not for judgment. In BC we offer software that provides doctors with their performance measures in the privacy of their own office. I believe this creates a safe environment where doctors can learn to improve the care they provide and that making the results public would detract from this process. G. Howard Platt Director Medical Outcomes Improvement Branch British Columbia Ministry of Health Victoria, BC
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.263 | 0.104 |
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".