Real-World Adherence to Guideline-Recommended Treatment for Small Cell Lung Cancer
Bibliographic record
Abstract
OBJECTIVES: The authors sought to quantify the treatment patterns and outcomes for limited-stage (LS) and extensive-stage (ES) small cell lung cancer (SCLC) in a real-world setting. METHODS: A review was conducted using the Glans-Look Research Database of patients with SCLC managed at a tertiary cancer center in Canada from 2010 to 2016. Adherence was defined as the commencement of planned SCLC treatment. Rate of compliance with the Alberta Health Services, American Society of Clinical Oncology, and National Comprehensive Cancer Network SCLC treatment guidelines was evaluated. Outcomes were analyzed using the Kaplan-Meier method and the Cox proportional hazards model. RESULTS: A total of 404 patients met our inclusion criteria, 31% were LS. The median age at first treatment receipt was 67 years. LS treatment consisted mostly of chemoradiation (62%). Chemoradiation and surgery±adjuvant predicted better survival (median, 32 and 40 mo, respectively) compared with no treatment. ES treatment consisted mostly of chemotherapy (90%). Chemotherapy and thoracic radiotherapy correlated with longer overall survival (13 vs. 9 mo, respectively) compared with chemotherapy alone. Prophylactic cranial irradiation receipt in LS (50%) and ES (20%) predicted favorable survivals than none (LS: hazard ratio, 0.48; 95% CI, 0.29-0.79; ES: hazard ratio, 0.48; 95% CI, 0.33-0.70). Approximately a quarter of relapsed LS and ES had second-line chemotherapy; improved survival with second line was observed only in ES (P<0.01). CONCLUSIONS: This study highlights high rates of guideline-recommended first treatment among the real-world LS and ES patients but it also revealed important outcome differences in relapsed LS and ES patients treated with second-line chemotherapy.
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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 | 0.011 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".