A Randomized Trial of the Electronic Lung Cancer Symptom Scale for Quality-of-Life Assessment in Patients with Advanced Non-small-Cell Lung Cancer
Bibliographic record
Abstract
Introduction: Improving health-related quality of life (hrqol) is a key goal of systemic therapy in advanced lung cancer, although routine assessment remains challenging. We analyzed the impact of a real-time electronic hrqol tool, the electronic Lung Cancer Symptom Scale (elcss-ql), on palliative care (pc) referral rates, patterns of chemotherapy treatment, and use of other supportive interventions in patients with advanced non-small-cell lung cancer (nsclc) receiving first-line chemotherapy. Methods: Patients with advanced nsclc starting first-line chemotherapy were randomized to their oncologist receiving or not receiving their elcss-ql data before each clinic visit. Patients completed the elcss-ql at baseline, before each chemotherapy cycle, and at subsequent follow-up visits until disease progression. Prospective data about the pc referral rate, hrqol, and use of other supportive interventions were collected. Results: For the 95 patients with advanced nsclc who participated, oncologists received real-time elcss-ql data for 44 (elcss-ql arm) and standard clinical assessment alone for 51 (standard arm). The primary endpoint, the pc referral rate, was numerically higher, but statistically similar, for patients in the elcss-ql and standard arms. The hrqol scores over time were not significantly different between the two study arms. Conclusions: The elcss-ql is feasible as a tool for use in routine clinical practice, although no statistically significant effect of its use was demonstrated in our study. Improving access to supportive care through the collection of patient-reported outcomes and hrqol should be an important component of care for patients with advanced lung cancer.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".