Surveying surgeon practices and perspectives on extent of intraoperative nodal evaluation in non–small cell lung cancer
Bibliographic record
Abstract
Objective The National Comprehensive Cancer Network and Commission on Cancer guidelines encourage surgeons to obtain tissue from 1 or more N1 and 3 N2 nodal stations during resection for non–small cell lung cancer. We aimed to characterize surgeons' familiarity with and adherence to recommended guidelines and to elucidate factors influencing surgical practices globally. Methods A questionnaire was designed to assess surgeon behaviors regarding intraoperative nodal assessment decisions during lung cancer resection. Survey items included demographics, case-based scenarios, self-perceived behaviors regarding nodal decision-making, and knowledge-based questions regarding nodal assessment guidelines. The survey was distributed to the General Thoracic Surgical Club, European Society of Thoracic Surgeons, Canadian Association of Thoracic Surgeons, and Australian & New Zealand Society of Cardiac & Thoracic Surgeons. Results Altogether, 236 of 2396 surgeons (9.8%) from 46 countries responded. The majority were men (192/236) and general thoracic surgeons (204/236). Participants were subcategorized into North America (n = 96), Europe (n = 96), and All Other (n = 44). The importance of 4 variables that impact lymph node excision varied by region: length of procedure ( P = .04), patient age ( P = .0004), patient frailty ( P = .0034), and institutional guidelines ( P = .01). Surgeons stated that in patients who received neoadjuvant treatment, most would opt for a full lymphadenectomy. A total of 80.5% (n = 190) claimed familiarity with guidelines, yet only 56.4% (n = 133) could identify the guidelines. Conclusions The variables driving intraoperative decision-making for nodal dissection vary by region. Moreover, surgeons tend to overstate their knowledge of existing guidelines. To optimize cancer care around the world, education needs to be provided uniformly to drive positive patient outcomes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".