Charting Errors in a Teaching Hospital
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to assess charting errors by junior trainees in the emergency department at the beginning of the academic year and to evaluate the effect of audits and reminders in reducing charting errors in July. METHODS: Medical records from June and July 2006 were reviewed to identify incomplete documentations (charting errors) in 5 areas. The audit was repeated in July 2007 after sample charts were displayed, and reminders were sent. RESULTS: There were 129 patient records completed by 12 trainees in June 2006 and 122 by 11 trainees in July 2006. The mean charting error rate for July (24%) was significantly higher than that in June (17%) (P = 0.0041). The mean charting error rate reduced to 14% after the intervention in July 2007. CONCLUSIONS: There is a significant increase in charting errors by new trainees in July compared with June. A simple intervention of reminders and alerts significantly reduced charting errors in July.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".