Developing model-based algorithms to identify screening colonoscopies using administrative health databases
Bibliographic record
Abstract
BACKGROUND: Algorithms to identify screening colonoscopies in administrative databases would be useful for monitoring colorectal cancer (CRC) screening uptake, tracking health resource utilization, and quality assurance. Previously developed algorithms based on expert opinion were insufficiently accurate. The purpose of this study was to develop and evaluate the accuracy of model-based algorithms to identify screening colonoscopies in health administrative databases. METHODS: Patients aged 50-75 were recruited from endoscopy units in Montreal, Quebec, and Calgary, Alberta. Physician billing records and hospitalization data were obtained for each patient from the provincial administrative health databases. Indication for colonoscopy was derived using Bayesian latent class analysis informed by endoscopist and patient questionnaire responses. Two modeling methods were used to fit the data, multivariate logistic regression and recursive partitioning. The accuracies of these models were assessed. RESULTS: 689 patients from Montreal and 541 from Calgary participated (January to March 2007). The latent class model identified 554 screening exams. Multivariate logistic regression predictions yielded an area under the curve of 0.786. Recursive partitioning using the latent outcome had sensitivity and specificity of 84.5% (95% CI: 81.5-87.5) and 63.3% (95% CI: 59.7-67.0), respectively. CONCLUSIONS: Model-based algorithms using administrative data failed to identify screening colonoscopies with sufficient accuracy. Nevertheless, the approach of constructing a latent reference standard against which model-based algorithms were evaluated may be useful for validating administrative data in other contexts where there lacks a gold standard.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".