Accuracy of Administrative Data in Identifying Ulcerative Colitis Patients Presenting with Acute Flare and Undergoing Colectomy: 2010 Presidential Poster
Bibliographic record
Abstract
Purpose: Administrative databases have been widely used to evaluate in-hospital outcomes among ulcerative colitis (UC) patients admitted for a flare. However, the validity of administrative data defining admission diagnoses in UC has not been adequately validated. This study evaluates the accuracy of International Classification of Diseases coding in identifying patients who are admitted for UC flare or undergo colectomy. Methods: Population-based surveillance was conducted in the Calgary Health Region between January 1, 1996 and December 31, 2007 using the Discharge Abstract Database to identify adults (≥18 years) admitted for UC. Two cohorts were identified: patients admitted for UC (ICD-9:556.X, ICD-10:K51.X) and patients admitted with UC who underwent colectomy (ICD-9:45.7, 45.8; ICD-10/CCI:1.NM.87, 1.NM.89, 1.NM.91, 1.NQ.89, 1.NQ.90). UC patients who did not undergo colectomy were stratified by diagnostic position (i.e. UC coded as the primary diagnosis vs. UC in diagnostic position 2 or 3). All medical charts were comprehensively reviewed and 100 charts were randomly audited to confirm agreement. The accuracy of the administrative data in correctly identifying patients presenting with UC flare and patients admitted for colectomy was assessed. Results: The administrative database identified 697 admissions of UC that underwent colectomy; 665 charts were available for review. The administrative data correctly identified both UC and colectomy in 85.9% [95% CI: 83.2-88.5%] of cases. Reasons for misclassification included: repeat admissions (3.8%); patients did not have UC (5.4%); and patients did not undergo colectomy (5.0%). Chart review was performed for 569 patients admitted for UC flare but without colectomy. UC was the primary diagnosis in 66.1%. Overall, the administrative data was 57.1% [53.1-61.2%] accurate in identifying patients presenting with a flare. 10 patients (1.8%) underwent colectomy, which was not recorded in the administrative data. Other reasons for misclassification included: prior colectomy (12.3%); patients without UC (10.4%), or UC was a comorbidity for an unrelated admission (18.5%). The accuracy of administrative data identifying a UC flare varied by diagnostic position: 79.3% [75.2-83.4%] in the primary diagnostic position; 18.7% [11.3-26.1%] in the second diagnostic position; and 8.1% [2.4-13.9%] in the third diagnostic position. Conclusion: Administrative data accurately identifies UC patients who underwent a colectomy, but inappropriately included a subset of patients without UC or colectomy and missed a small proportion of colectomy patients. Administrative data is less reliable in identifying UC patients presenting to hospital with a flare, particularly if UC is not coded in the primary diagnostic position.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".