Practice patterns in the treatment of locally advanced resectable esophageal carcinoma: A national survey of Canadian thoracic surgeons.
Bibliographic record
Abstract
e14695 Background: Many institutions have adopted a multimodality strategy for treating locally advanced resectable esophageal cancer including surgery, chemotherapy and radiation. While many neoadjuvant protocols have been studied, there is no well defined standard treatment. In order to determine current practices and clinician opinions regarding treatment of esophageal cancer, a survey study of practicing Canadian thoracic surgeons was performed. Methods: Members of the Canadian Association of Thoracic Surgeons were contacted by email; those who currently treat esophageal cancer were asked to complete an online survey. Three separate emails were sent to maximize participation. Results: The response rate was 54% (56 /104). Of the respondents, 85% exclusively practiced general thoracic surgery, 87% worked at a University-affiliated hospital. We presented a hypothetical patient with bulky, resectable distal esophageal adenocarcinoma with enlarged paraesophageal lymph nodes (T3N1M0). 54% stated that neoadjuvant chemoradiation followed by surgery was their institution’s treatment of choice, while 33% used neoadjuvant chemotherapy plus surgery. When asked to choose the best treatment for this patient based on available evidence, 33% chose neoadjuvant chemoradiation, 33% favored neoadjuvant chemotherapy, 31% were undecided. Regarding neoadjuvant chemotherapy vs. chemoradiation, 63% strongly agreed or agreed there was insufficient evidence to decide whether or not one treatment was superior to the other. 73% strongly agreed or agreed to support a future randomized trial of preoperative chemotherapy vs. preoperative chemoradiation for esophageal cancer patients. Conclusions: Most Canadian thoracic surgeons use either neoadjuvant chemotherapy or chemoradiation followed by surgery for locally advanced resectable esophageal cancer. There is wide variation in practice patterns with no clear standard approach. 63% feel there is insufficient evidence to decide whether or not one treatment is superior to the other, and the majority support a future trial of neoadjuvant chemotherapy vs. chemoradiation. A pilot study is being planned to determine feasibility.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".