Review Article Open Access Volume 3 • Issue 3 • 1000113 J Health Med Inform
Bibliographic record
Abstract
Adverse events undeniably compromise patient safety. Inadvertent complications attributable to preventable error rather than patient illness or disease suggests a fragmented and dire system of care. Adverse events by definition refer to diagnostic errors, and make up a substantial fraction of all medical errors leading to unnecessary morbidity, deaths, and healthcare costs. The Canadian Adverse Events Study published in 2004 and conducted by the Canadian Institute of Health Information found that the overall rate of adverse events in 2000 was seven point five per one hundred patients admitted to Canadian hospitals, one point six of which were associated with causing death [1]. “About 1 out of every 10 patients with health problems surveyed in Canada reported that they had been given a wrong medication or a wrong dose in the past two years. Three out of every 20 Canadians surveyed reported an adverse event; about 46 % of these resulted in a serious health problem ” [2].
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.270 | 0.073 |
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".