Chart documentation quality and its relationship to the validity of administrative data discharge records
Bibliographic record
Abstract
The validity of administrative data may be vulnerable to how well physicians document medical charts. The objective of this study is to determine the relationship between chart documentation quality and the validity of administrative data. The charts for patients who underwent carotid endarterectomy were re-abstracted and rated for the quality of documentation. Poorly and well-documented charts were compared by patient, physician, and hospital variables, as well as on agreement between the administrative and re-abstracted data. Of the 2061 charts reviewed, 42.6 per cent were rated well documented. The proportion of charts well documented varied from 14.6 to 87.5 per cent across 17 hospitals, but did not vary significantly by patient characteristics. The kappa statistic was generally higher for well-documented charts than for poorly documented charts, but varied across comorbidities. In conclusion, poorly documented hospital charts tend to be translated into invalid administrative data, which reduces the communication of clinical information among healthcare providers.
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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 | 0.064 | 0.502 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".