Assessments to Ensure Quality of Notes during Transfer of Patient Care
Bibliographic record
Abstract
In 1948, Life magazine published a classic photo essay titled "The Country Doctor," documenting the everyday life of Dr. Ernest Ceriani, a general practitioner who provided 24-hour care to a community of 2,000 inhabitants.The poignant blackand-white photos reveal an immensely rewarding life while simultaneously betraying his exhaustion due to long hours (1).More than eight decades later, medicine has evolved in small and large communities as a result of many factors.Although Dr. Ceriani didn't transfer the care of his patients often, two changes have demanded an increase in the number and intensity of patient handovers: work hour restrictions and patient complexity (2).Patient care is thus critically dependent on the quality of a written or verbal handover; indeed, poor handover can result in significant errors in patient care (2).These issues are more acute in the intensive care unit (ICU) setting, a fastpaced environment in which exceedingly complex patients are treated (3).Although there are gaps in training and assessment for verbal and written handovers, the most significant gap in the
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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 | 0.039 | 0.181 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.040 | 0.048 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".