Validation of colorectal cancer surgery data from administrative data sources
Bibliographic record
Abstract
BACKGROUND: Surgery is the primary treatment for colorectal cancer for both curative and palliative intent. Availability of high quality surgery data is essential for assessing many aspects of the quality of colorectal cancer care. The objective of this study was to determine the quality of different administrative data sources in identifying surgery for colorectal cancer with respect to completeness and accuracy. METHODS: All residents in Alberta, Canada who were diagnosed with invasive colorectal cancer in years 2000-2005 were identified from the Alberta Cancer Registry and included in the study. Surgery data for these patients were obtained from the Cancer Registry (which collects the date of surgery for which the primary tumor was removed) and compared to surgery data obtained from two different administrative data sources: Physician Billing and Hospital Inpatient data. Sensitivity, specificity, positive predictive value, negative predictive value and observed agreement were calculated compared to the Cancer Registry data. RESULTS: The Physician Billing data alone or combined with Hospital Inpatient data demonstrated equally high sensitivity (97% for both) and observed agreement with the Cancer Registry data (93% for both) for identifying surgeries. The Hospital Inpatient data, however, had the highest specificity (80%). The positive predictive value varied by disease stage and across data sources for stage IV (99% for stages I-III and 83-89% for stage IV), the specificity is better for colon cancer surgeries (72-85%) than for rectal cancer surgeries (60-73%); validation measures did not vary over time. CONCLUSION: Physician Billing data identify the colorectal cancer surgery more completely than Hospital Inpatient data although both sources have a high level of completeness.
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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 | 0.039 | 0.159 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".