Stereotactic Body Radiotherapy (SBRT) in Very Limited-Stage Small Cell Lung Cancer (VLS-SCLC)
Bibliographic record
Abstract
Small cell lung cancer (SCLC) is an aggressive neuroendocrine tumour with metastatic propensity. Stereotactic body radiation therapy (SBRT) is an emerging therapeutic option for SCLC, despite limited supporting evidence. By evaluating the use of SBRT in very limited stage (VLS) SCLC at our institution, we aimed to contribute to the existing knowledge in this area while establishing a basis for further research. We performed a retrospective review of all cases of VLS-SCLC treated with SBRT between 2013 and 2020. Baseline demographics, diagnostic, and treatment information were collected. The primary outcome was overall survival (OS). We identified 46 patients with pathologically confirmed VLS-SCLC; 25 were treated with SBRT, and the remainder received either surgery, conventional radiation therapy, chemotherapy, or palliative-intent therapy. After a median follow-up of 23.7 months, 44% of the patients had died; the median OS was of 24.4 months for the SBRT cohort and 67.0 months for the curative intent non-SBRT cohort. The difference in disease recurrence and survival between cohorts was underpowered and not statistically significant. Higher baseline ECOG and comorbidity was noted in the SBRT cohort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".