A paired-comparison intervention to improve quality of medical records
Bibliographic record
Abstract
Background: To evaluate the quality of medical record (MR) compilation in the Teaching Hospital of the Second University of Naples, Italy, after a controlled intervention for quality improvement. Methods: From the 66 wards of the Teaching Hospital, we selected eight homogeneous pairs of wards, matched for similar typology. For each pair, we randomized a ward to undergo a training course about correct compilation of MRs (treated group) and considered the remaining ward as a control (untreated group). For each section of MR we evaluated completeness and clarity of handwriting and presence and clarity of signature. Results: In general, the worst result in both groups was the absence of signature in the daily diary (76.6% in the treated group and 94.4% in the untreated group). The greatest differences between the two groups were detected in the compilation of the daily diary (absent/incomplete in 1.9% of the treated group compared with 21.9% of the untreated group; relative risk [RR] = 11, 95% confidence interval [CI] = 5.1-26.4) and the physical examination section (absent/incomplete in 2.8% of the treated group compared with 21.3% of the untreated group; RR = 7.5; 95% CI = 3.8-14.8). Conclusions: Comparison between the treated and untreated groups shows that there is a significant improvement in compilation of several sections of the MRs in the treated group. However, the results obtained were only partially satisfactory because of the poor quality of MR compilation in both groups.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".