Barriers to Transanal Endoscopic Surgery Referral in Canada
Bibliographic record
Abstract
Abstract Backgroud: Transanal endoscopic surgery (TES) is a minimally invasive procedure that allows for full thickness local excision of adenomas and select early rectal adenocarcinomas. Despite its advantages, TES is not uniformly utilized across Canada. Methods Potential barriers to TES referral were explored via a survey distributed to endoscopists across Canada, using a stratified sampling method. Results In total, 199/501 endoscopists completed our survey, including 62 (31%) gastroenterologists and 136 (69%) surgeons, consistent with a 39% response rate. For patients with clear and unclear indications for TES, 30/146 (27%) and 64/146 (44%) of referring endoscopists have a low referral rate, respectively. On univariable analysis, factors associated with low referral rate include lack of confidence with indications for TES [OR 9.9 (CI 3.15–31.4) p < 0.001], poor understanding regarding the advantages of TES [OR 11.3 (CI 3.83–33.1) p < 0.001], low comfort with referring [OR 183.7 (CI 21.9-1537.5) p < 0.001], distance greater than one hour from a TES surgeon [OR 5.786 (CI 2.63–12.8) p < 0.001] and lack of access to TES [OR 7.8 (CI 3.34-18.0) p < 0.001]. Gastroenterologists are more likely to have a low referral rate than surgeons [OR 2.76 (CI 1.30–5.8) p < 0.01]. On multivariable analysis, low comfort with referring [OR 67.4 (CI 5.8-779.8) p < 0.001] and greater distance to a TES surgeon [OR 4.5 (CI 1.17–16.9 p < 0.001)] remained independently associated with low referral rate. Provinces with a population of > 1 million [OR 3.66 (CI 1.49-9.0) p < 0.01], academic practice settings [OR 3.05 (CI 1.29–7.3) p < 0.05], and surgeon endoscopists [OR 4.5 (CI 1.68, 12.1) p < 0.01] were all independently associated with greater TES accessibility. Conclusions Many patients who are potentially eligible for TES are not being referred for consideration. An educational gap regarding indications, lack of comfort among referring physicians and geographic inaccessibility are among the greatest barriers to referral.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".