Coding of heart failure diagnoses in Saskatchewan: a validation study of hospital discharge abstracts.
Bibliographic record
Abstract
BACKGROUND: Validity of Heart Failure (HF) diagnoses from administrative records has not been extensively evaluated, especially with respect to small / unselected hospitals. OBJECTIVES: To determine the positive predictive value of a primary / most responsible diagnosis of HF among a general population of subjects discharged from Saskatchewan hospitals. METHODS: Using administrative health records from the Province of Saskatchewan, Canada, we identified subjects experiencing their first HF hospitalization between 1994 and 2003. From this cohort, we randomly selected 500 subjects for individual validation using Framingham and Carlson criteria. RESULTS: The 466 charts available for analysis, 74% (345/466) and 63.9% (298/466) of subjects met criteria for a clinical diagnosis of HF based on Framingham or Carlson criteria, respectively; 57.5% (268/466) met both criterion. Provincial hospitals (located in the largest urban centres) were associated with the highest proportion of confirmed HF diagnoses (87.8% by Framingham criteria) compared to progressively smaller hospitals (regional 77.9%; district 64.2%; and community 60.0%). Accuracy also differed when stratified by physician category. Cardiologists and internists were associated with the highest rates of confirmed diagnoses [(97.5% (39 / 40) and 85.0% (34 / 40)]) compared to general practitioners [(73.1% (95 / 130)]) and other physicians [(69.1% (177 / 256)]), by Framingham criteria. CONCLUSIONS: Hospital discharge abstracts indicating HF are frequently inaccurate. These findings have important implications for the epidemiologic study of HF as well as the clinical management of patients.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".