Bibliographic record
Abstract
Accuracy of the information in health care database systems is essential throughout the health care system for functions such as planning and research. This study examines the reliabihty of the HMRI database, recently amalgamated under the CIHI. It compares selected data items from the in-patient hospital discharge database against a re-abstracted set of data items from the original health record in a sample of six Vancouver acute care hospitals for the fiscal year 31 March 1986 - 1 April 1987. Cases were restricted to acute care medical and surgical cases. A total of 606 cases using the ICD-9 classification system were re-abstracted using the original health record. Results demonstrated nondiagnostic variables demonstrated an overall agreement of 92.4%. The agreement for the Most Responsible Diagnosis (MRD) to four-digits is 61.4%, while individual hospital scores ranged from 52.0 to 69.6%. For the MRD to three-digits agreement increased to 72.1%, with individual hospitals ranging between 62.4 and 79.4%. The Principal Procedure (PP) agreement to three-digits was 64.8% with individual hospital scores ranging from 56.3 to 75.6%. For the PP at two-digits, agreement was 72.9%, with individual hospitals ranging from 61.4 to 85.4%. Denominators for secondary diagnoses and secondary procedures reflect the total number of diagnoses and/or procedures recorded. Secondary diagnoses to four-digits had agreement scores of 67.4% by number of diagnoses recorded and secondary procedures to three-digits of 80.7% by number of secondary procedures recorded. Total diagnoses and procedures combined demonstrated an overall agrement score of 68.3% with individual hospitals ranging from 61.8 to 73.1%. Agreement by case, where all relevant diagnostic and procedural codes in the entire record matched, dropped significantly to 34.5% for secondary diagnoses and to 59.7% for secondary procedures. The greatest frequency overall for the type of discrepancy was for clerical errors, especially for code books not used properly to determine specificity of the diagnosis. Specificity of the code is required, the information is available in the record, but specificity is not determined by the coder. The greatest frequency of discrepancy for the MRD was 73.1 % for clerical errors. For the PP, 46.9% of discrepancies were in the selection of principal procedure and 42.9% for clerical errors. This study did not demonstrate a significant difference between individual coders by years of experience, by credentials or by years of experience and credentials. It was deterrnined that the data are unsuitable for a quality of care study where the data are utilized beyond the individual hospital site. Care must be taken when utilizing those data for research purposes.
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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 | 0.061 | 0.235 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".