Is lobectomy superior to sublobar resection for early-stage small-cell lung cancer discovered intraoperatively?
Bibliographic record
Abstract
A best evidence topic in thoracic surgery was written according to a structured protocol. The question addressed was: Is lobectomy superior to sublobar resection (SLR) for early-stage (cT1/2N0) small-cell lung cancer (SCLC) discovered intraoperatively? Altogether, more than 360 papers were found using the reported search, of which 10 represented the best evidence to answer the clinical question. The authors, journal, date and country of publication, patient group studied, study type, relevant outcomes and results of these papers are tabulated. Surgical treatment was shown to be superior to non-surgical treatment for early-stage SCLC in 8 papers. Seven papers showed that among patients treated surgically, lobectomy is associated with improved survival compared to SLR. One paper demonstrated both improved survival and improved freedom from local recurrence. However, 1 paper showed no difference when lobectomy was compared to anatomical segmentectomy. Three papers demonstrated significant rates of upstaging in surgical patients. Although both lobectomy and SLR are associated with improved survival compared with non-surgical treatment in early-stage SCLC, lobectomy is superior. Lobectomy was associated with improved median and overall survival, better upstaging and decreased local recurrence compared to SLR, although there is potential for selection bias and stage migration. Lobectomy should be considered the optimal approach for patients with early-stage SCLC.
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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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".