Referral patterns for common surgical procedures in Ontario: a cross-sectional population-level study
Bibliographic record
Abstract
BACKGROUND: Little is known about the existing structure and function of referral networks in the prevalent referral system for specialized surgical care in Canada, which is based on direct physician referral to specialists in a largely unmanaged referral marketplace. Our objective was to describe and analyze the referral networks of referring physicians and surgeons for common surgical procedures in Ontario, to better understand potential barriers to single-entry models. METHODS: We analyzed referral networks for patients between referring physicians and surgeons for 9 common scheduled surgical procedures from 2016 to 2019 using administrative data sources in Ontario. We described the connectedness of referring physician-surgeon pairs using descriptive measures and graphical social network analysis. RESULTS: The median number of surgeons connected to a referring physician for patients having a particular surgical procedure ranged from 1 (interquartile range [IQR] 1-3) for spine surgery to 3 (IQR 1-4) for knee arthroplasty and 3 (IQR 2-5) for noncancer uterine procedures. Referral network structure varied according to the procedure studied. Spine surgery was highly clustered with a small number of larger groups; gallbladder, inguinal hernia, and noncancer uterine surgery were highly distributed with many small groups within the referral network. Breast cancer surgery occurred in a largely distributed network, but with a skewed distribution reflecting a few small groups with large numbers of patients. CONCLUSION: Improving surgical wait times by coordinating surgical referrals will require approaches that address the structure of existing referral networks. Most physicians refer their patients to a very small number of surgeons, suggesting that referring physicians largely do not individualize referrals to multiple different surgeons based on specific patient characteristics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".