Defining non-inferiority margins for quality of surgical resection for rectal cancer.
Bibliographic record
Abstract
e18760 Background: A key challenge for non-inferiority randomized clinical trials (NI-RCTs) is the generation of non-inferiority margins (ΔNI). Guidance for the selection of ΔNI is directed to pharmaceuticals with no clear recommendations for NI-RCTs of operative procedures. Quality of surgical resection metrics have been used as surrogates for long-term oncologic outcomes in NI-RCTs comparing laparoscopic and open surgery for rectal cancer. However, ΔNI for these outcomes have not been defined. Methods: A 2-round web-based Delphi process was used to define ΔNI for quality of surgical resection metrics: positive circumferential resection margin (CRM), incomplete plane of mesorectal excision (PME), positive distal resection margin (DRM), and a composite of these outcomes. Between September 2016 and February 2017, 134 international experts in rectal cancer (68 surgeons, 20 medical oncologists, 16 radiation oncologists, and 27 pathologists) were invited to participate. Experts were presented with evidence syntheses summarizing the association between quality of surgical resection and long-term outcomes, and pooled quality of surgical resection outcomes for open surgery. Experts were then asked to provide ΔNI for all outcomes balancing the risk and benefits of minimally-invasive surgery. Results: 72 experts participated: 57 completed the initial questionnaire (Round 1) and 58 the revised questionnaire (Round 2). Consensus was reached for all individual ΔNI but not for the composite. The mean (SD) ΔNI was an absolute difference of 2.33% (1.59) for proportion of positive CRM when comparing surgical interventions for treatment of rectal cancer; 2.85% (1.83) for incomplete PME; 1.28% (1.13) for positive DRM; and 2.71% (2.28) for the composite. However, opinions varied widely for the composite outcome. Conclusions: Delphi processes are a feasible approach to generate ΔNI to evaluate novel surgical approaches. The generated ΔNI for quality of surgical resection metrics for rectal cancer can be used for future NI-RCTs. Outcome Rate for Open Resection (%) Round 1 Mean ΔNI (SD) Round 2 Mean ΔNI (SD) CRM 9.0 2.94 (2.29) 2.33 (1.59 PME 8.6 3.55 (2.66) 2.85 (1.83) DRM 1.5 2.09 (2.36) 1.28 (1.13) Composite 87.4 4.49 (3.09) 2.71 (2.28)
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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 | 0.435 | 0.583 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".