<scp>MDT</scp> lung cancer care: Input from the <scp>S</scp>urgical <scp>O</scp>ncologist
Bibliographic record
Abstract
Although there have been many advancements in the multidisciplinary management of non-small cell lung cancer (NSCLC), surgery remains the primary modality of choice for resectable lung cancer when the patient is able to tolerate lung resection physiologically. There have been recent advances in surgical diagnosis and treatment of lung cancer. Increasing use of low-dose computed tomography (CT) screening for lung cancer has resulted in increased detection of small peripheral nodules or semi-solid ground glass opacities. Here, we review different modalities of localization techniques that have been used to aid surgical excisional biopsy when needle biopsy has failed to provide tissue diagnosis. We also report on the current debates regarding the use of sublobar resections for Stage I NSCLC as well as the surgical management of locally advanced NSCLC. Finally, we discuss the complex surgical management of T4 NSCLC lung cancers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".