A Randomized Controlled Trial to Improve Lymph Node Assessment in Stage II Colon Cancer
Bibliographic record
Abstract
HYPOTHESIS: Physicians seem to learn best from their peers, yet the impact of opinion leaders on physician behavior is unclear. Because colon cancer staging has been identified as being suboptimal in Ontario, Canada, we sought to evaluate the influence of expert and local opinion leaders for colon cancer on optimizing colon cancer lymph node assessment. DESIGN, SETTING, PARTICIPANTS: A cluster-randomized trial including all hospitals in Ontario that identified a local opinion leader with intervention between January 5 and June 17, 2004. INTERVENTION: All 42 centers received a standardized lecture about colon cancer lymph node assessment delivered by an expert opinion leader in colon cancer. The 21 intervention hospitals also received academic detailing of a local opinion leader by the expert opinion leader and a toolkit. MAIN OUTCOME MEASURES: Mean number of lymph nodes assessed in patients with stage II colon cancer and the proportion of cases staged with a minimum of 12 lymph nodes before and after a standardized lecture were assessed. RESULTS: Patient demographic and tumor factors were similar in both groups before and after the standardized lecture. Lymph node assessment significantly improved after the standardized lecture at intervention and control sites (P < .001). No additional benefit of academic detailing and toolkit provision in the intervention was demonstrated. CONCLUSIONS: In-person provision of information by an expert opinion leader in colon cancer may stimulate performance regarding lymph node assessment for colon cancer. Academic detailing of a local opinion leader did not further improve lymph node assessment.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".