Management of Very Early Small Cell Lung Cancer: A Canadian Survey Study
Bibliographic record
Abstract
Concurrent chemoradiotherapy (CRT) is the standard of care for limited-stage small cell lung cancer (LS-SCLC). Local therapy—surgery or stereotactic body radiotherapy (SBRT)—with adjuvant chemotherapy may be appropriate for very early (T1-T2, N0) disease. There is variability in the management of these cases, which may lead to variability in patient outcomes. This study aimed to determine practice patterns for the management of very early LS-SCLC in Canada. A survey was developed and distributed to Canadian medical and radiation oncologists specialising in lung cancer. The survey consisted of three sections: (1) physician demographics, (2) general practice approach, and (3) preferred approach for three clinical scenarios (1: peripheral T1 lesion; 2: central T1 lesion; 3: peripheral T2 lesion). Responses were analysed to detect differences across cases and among physician groups. There were 77 respondents. In case 1, assuming medical operability, most respondents (73%) chose surgery and adjuvant chemotherapy, with 19% choosing CRT. CRT was selected by a higher proportion in case 2 (48%) and case 3 (61%) (p < 0.05). If medically inoperable, most chose CRT over local therapy in all cases, with more choosing CRT in case 2 (84%) and case 3 (86%) than in case 1 (55%) (p < 0.05). Subgroup analysis showed a predilection towards CRT in Western Canada and among more experienced physicians, and towards SBRT in Ontario. There is variability in the management of very early LS-SCLC in Canada. CRT remains the most popular strategy in most cases, with surgery preferred for small peripheral lesions. Larger and more central tumours are more likely to be managed with CRT. Variation in practice is correlated with region and physician experience. Our study illustrates the variability in the management of very early LS-SCLC in Canada and highlights the need for more robust investigations into the ideal approach for these patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".