Validation of use of ramq databases for chronic obstructive pulmonary disease patients
Bibliographic record
Abstract
The Regie de 1'assurance maladie du Quebec (RAMQ) health insurance databases have been demonstrated to be a valuable research tool for certain illnesses. The purpose of this study was to examine which data from the RAMQ administrative database could be used to accurately diagnose COPD patients, and classify their severity and comorbidity. Methods . Patients with physician-diagnosed COPD were selected using hospital discharge and outpatient records. Information collected from medical chart was compared to information on the same patients from medical service and prescription drug databases. Results. The ICD-9 respiratory diagnostic codes were found to have a sensitivity of 93% identifying COPD in 151 cases and 94% specificity within a group of similar-aged asthmatic patients. The number of prednisone prescriptions over one year accurately separated severe from moderate patients. As well, diagnostic codes showed moderate reliability for indicating the presence of comorbidity. Conclusions. The diagnostic codes of the medical service database were accurate at identifying COPD patients and the prescription drug data was useful for classifying their severity.
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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 | 0.020 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".