Monitoring trends in waiting periods in Canada for elective surgery: validation of a method using administrative data.
Bibliographic record
Abstract
BACKGROUND: Provincial governments require timely, economical methods to monitor surgical waiting periods. Although use of prospective procedure-specific registers would be the ideal method, a less elaborate system has been proposed that is based on physician billing data. This study assessed the validity of using the date of the last service billed prior to surgery as a proxy for the beginning of the post-referral, pre-surgical waiting period. METHOD: We examined charts for 31,824 elective surgical encounters between 1992 and 1996 at an Ontario teaching hospital. The date of the last service before surgery (the last billing date) was compared with the date of the consultant's letter indicating a decision to book surgery (i.e., to begin waiting). RESULTS: Several surgical specialties (but excluding cardiac, orthopedic and gynecologic) had a close correlation between the dates of the last pre-surgery visit and those of the actual decision to place the patient on the waiting list. Similar results were found for 12 of 15 individually studied procedures, including some orthopedic and gynecological procedures. CONCLUSION: Used judiciously, billing data is a timely, inexpensive and generally accurate method by which provincial governments could monitor trends in waiting times for appropriately selected surgical procedures.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".