Management of Small-Cell Lung Cancer with Radiotherapy—A Pan-Canadian Survey of Radiation Oncologists
Bibliographic record
Abstract
BACKGROUND: The management of small-cell lung cancer (sclc) with radiotherapy (rt) varies, with many treatment regimens having been described in the literature. We created a survey to assess patterns of practice and clinical decision-making in the management of sclc by Canadian radiation oncologists (ros). METHODS: A 35-item survey was sent by e-mail to Canadian ros. The questions investigated the role of rt, the dose and timing of rt, target delineation, and use of prophylactic cranial irradiation (pci) in limited-stage (ls) and extensive-stage (es) sclc. RESULTS: Responses were received from 52 eligible ros. For ls-sclc, staging (98%) and simulation or dosimetric (96%) computed tomography imaging were key determinants of rt suitability. The most common dose and fractionation schedule was 40-45 Gy in 15 once-daily fractions (40%), with elective nodal irradiation performed by 31% of ros. Preferred management of clinical T1/2aN0 sclc favoured primary chemoradiotherapy (64%). For es-sclc, consolidative thoracic rt was frequently offered (88%), with a preferred dose and fractionation schedule of 30 Gy in 10 once-daily fractions (70%). Extrathoracic consolidative rt would not be offered by 23 ros (44%). Prophylactic cranial irradiation was generally offered in ls-sclc (100%) and es-sclc (98%) after response to initial treatment. Performance status, baseline cognition, and pre-pci brain imaging were important patient factors assessed before an offer of pci. CONCLUSIONS: Canadian ros show practice variation in sclc management. Future clinical trials and national treatment guidelines might reduce variability in the treatment of early-stage disease, optimization of dose and targeting in ls-sclc, and definition of suitability for pci or consolidative rt.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".