<p>Prediction of chemotherapy adverse reactions and mortality in older patients with primary lung cancer through frailty index based on routine laboratory data</p>
Bibliographic record
Abstract
Objectives: To assess the role of a pre-chemotherapy frailty index based on routine laboratory data in predicting mortality and chemotherapy adverse reactions among older patients with primary lung cancer. Design: Retrospective cohort study Setting: West China Hospital, Chengdu, China Participants: We included patients aged ≥60 years with primary lung cancer receiving the first course of chemotherapy. Measurements: Data were collected from medical records, local government death databases or telephone interviews. Outcomes included chemotherapy adverse reactions and all-cause mortality. We constructed a frailty index based on 44 laboratory variables (FI-LAB) before chemotherapy, and chose the following cutoff points: robust (0.0–0.2), pre-frail (0.2–0.35) and frail (≥0.35). Results: We included 1,020 patients (71.4% male; median age: 65 years old). Both pre-frailty and frailty was associated with any chemotherapy adverse reactions and infections during chemotherapy (OR=3.48, 95%CI: 1.77–6.87; OR=3.58, 95%CI: 1.55–8.26, respectively). Frail patients had a shorter median overall survival rate compared to robust patients (18.05 months vs 38.89 months, log-rank p <0.001). After adjusting for some potential confounding variables, the risk of all-cause mortality was dramatically increased in frail patients (HR:2.13, 95% CI:1.51–3.00) with an average follow-up of 3.9 years. Each 0.01 or per standard deviation (SD) increase in the FI-LAB value significantly increased the HR of death by 2.0% (HR:1.02, 95% CI: 1.01–1.03) and 23.0% (HR: 1.23, 95% CI: 1.13–1.34), respectively. Conclusions: Frailty assessed by routine laboratory data indicates increased risks of chemotherapy adverse reactions and death in older patients with primary lung cancer receiving the first course of chemotherapy. Keywords: frailty, prognosis, older patients, lung cancer, 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".