Self-Reported Awareness and Use of<i>International Classification of Disease</i>Coding of Inflammatory Bowel Disease Services by Ontario Physicians
Bibliographic record
Abstract
RATIONALE: Population and health services research can be performed by linkage analysis of administrative data. However, the robustness of study results is determined by the accuracy of the diagnostic coding. OBJECTIVES: To estimate the awareness, use and accuracy of the International Classification of Diseases, Ninth Revision (ICD-9) coding by physicians providing services for patients with Crohn's disease (CD) and ulcerative colitis (UC). METHODS: All Ontario gastroenterologists and a 10% random sample of internists, pediatricians, pediatric or general surgeons, and family physicians were surveyed by postal questionnaire to estimate the frequency and 95% CI of using codes 555 or 556 when billing for CD- and UC-related services, respectively. c2 tests were used for between-group comparisons. RESULTS: Of the physicians who were surveyed, 67.7% (416 of 614) responded; 258 of 391 (66%) who were still practising in Ontario saw patients with inflammatory bowel disease (IBD), and 54% had more than 10 IBD patients; 86.5% (95% CI 82.4% to 90.6%) were familiar with ICD-9 codes, and 91.4% (95% CI 88.1% to 95.6%) used the codes 555 (CD) or 556 (UC) for billing. Rates of ICD-9 use did not differ by sex but were used more frequently by those graduating after 1981 (P<0.02). Gastroenterologists used ICD-9 IBD codes 555 or 556 significantly more often than all other physicians (P=0.001). Most (more than 75%) Ontario physicians used ICD-9 IBD codes always or frequently when billing for IBD-related services. Few (10%) used these codes to bill for non-IBD-related problems. CONCLUSIONS: These data suggest that there is acceptable use and accuracy of ICD-9 diagnostic coding for CD and UC services - comparable with results from studies of other diseases. Administrative data may thus be used to undertake epidemiological studies in IBD in Ontario.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".