Factors Associated with Referral to Medical Oncology and Subsequent Use of Adjuvant Chemotherapy for Non-Small-Cell Lung Cancer: A Population-Based Study
Bibliographic record
Abstract
BACKGROUND: Adjuvant chemotherapy (act) for non-small-cell lung cancer (nsclc) is associated with improved survival in the general population, but may be underutilized. We explored the factors associated with referral to medical oncology and subsequent use of act among all patients with resected nsclc in Ontario, Canada. METHODS: The Ontario Cancer Registry was used to identify all incident cases of nsclc diagnosed in Ontario during 2004-2006. We linked electronic records of treatment and of physician billing to identify surgery, act, and medical oncology consultation. A multivariate logistic regression model was used to evaluate factors associated with referral to medical oncology and subsequent use of act. RESULTS: Among 3354 cases of nsclc resected in Ontario during 2004-2006, 1830 (55%) were seen postoperatively by medical oncology, and 1032 (31%) were treated with act. Patients more than 70 years of age were less likely than younger patients to have a consultation [odds ratio (or): 0.4; p < 0.001]. A higher proportion of cases with stage ii or iii nsclc than with stage i disease were referred (ors: 2.7, 2.0 respectively; p < 0.005). We observed substantial geographic variation in the proportion of surgical cases referred (range: 32%-88%) that was not explained by differences in case mix. Among cases referred to medical oncology, older patients (age 60-69 years, or: 0.4; age 70+ years, or: 0.1; p < 0.001) with greater comorbidity (Charlson comorbidity index: 3+; or: 0.5; p < 0.05) and a longer postoperative stay (median length of stay: 7+ days; or: 0.7; p = 0.001) were less likely to receive act. Use of act was greater in patients with stage ii or iii than with stage i disease (ors: 3.0, 2.7 respectively; p < 0.001); use also varied with geographic location (range: 46%-63%). CONCLUSIONS: The initial decision to refer to medical oncology is associated with age and stage of disease, and those factors have an even greater effect on the decision to offer act. Comorbidity and postoperative length of stay were not associated with initial referral, but were associated with use of act in patients seen by medical oncology.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".