Quality of Hospital Discharge and Physician Data for Type of Breast Cancer Surgery
Bibliographic record
Abstract
OBJECTIVE: The quality of coding for breast surgical procedures was examined by comparing hospital discharge abstracts and physician claims with data abstracted from records of women diagnosed with node-negative breast cancer from April 1, 1991, to December 31, 1991. METHODS: The node-negative breast cancer cohort was linked with a population registry file. Hospital discharge abstracts and physician billing claims were retrieved for matched subjects. Overall agreement between two data sets was defined as the number of cases for which there was a match by specific type of procedure out of all eligible cases that were matched with the health care utilization file. Specific agreement was assessed by the kappa statistic, using only those records in the administrative data set that were coded for mastectomy or breast-conserving surgery. RESULTS: Of 735 eligible cases in the node-negative breast cancer cohort, 655 (89.1%) were linked to a health care utilization file. Overall agreement between surgeon billing claims and charts was 95.4% (CI = 93.5, 96.9) for most definitive procedure. Agreement for breast surgery type was 98.1% (kappa = 0.96; CI = 0.87,1.0) for cases coded as breast-conserving surgery or mastectomy. When hospital discharge and chart data were compared, overall agreement was 86.2% (CI = 83.4, 88.8), whereas agreement for breast surgery type was 93.2% (kappa = 0.86; CI = 0.77, 0.94). CONCLUSION: Overall, definitive surgical procedure in the two administrative databases accurately reflected information recorded in patients' charts. Physician claims appeared to provide more accurate information than did hospital discharge data.
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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 | 0.000 | 0.000 |
| 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".