Symptom Assessment Following Surgery for Lung Cancer
Bibliographic record
Abstract
OBJECTIVE: To conduct a population-level analysis of temporal trends and risk factors for high symptom burden in patients receiving surgery for non-small cell lung cancer (NSCLC). BACKGROUND: A population-level overview of symptoms after curative intent surgery is necessary to inform decision making and supportive care for patients with lung cancer. METHODS: Retrospective cohort study of patients receiving surgery for stages I to III NSCLC between January 2007 and September 2018. Prospectively collection Edmonton Symptom Assessment System (ESAS) scores, linked to provincial administrative data, were used to describe the prevalence, trajectory, and predictors of moderate-to-severe symptoms in the year following surgery. RESULTS: A total of 5350 patients, with 28,490 unique ESAS assessments, were included in the analysis. Moderate-to-severe tiredness (68%), poor wellbeing (63%), and shortness of breath (60%) were the most common symptoms reported. The rise and fall in the proportion of patients experiencing moderate-to-severe symptoms after surgery coincided with the median time to first (58 days, interquartile range: 47-72) and last cycle of chemotherapy (140 days, interquartile range: 118-168), respectively. There was eventual stabilization, albeit above the preoperative baseline, within 6 to 7 months after surgery. Female sex (relative risk [RR] 1.09- 1.26), lower income (RR 1.08-1.23), stage III disease (RR 1.15-1.43), adjuvant therapy (RR 1.09-1.42), chemotherapy within 2 weeks of an ESAS assessment (RR 1.14-1.73), and pneumonectomy (RR 1.05-1.15) were associated with moderate-to-severe symptoms following surgery. CONCLUSIONS: Knowledge of population-level prevalence, trajectory, and predictors of moderate-to-severe symptoms after surgery for NSCLC can be used to facilitate shared decision making and improve symptom management throughout the course of illness.
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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 | 0.004 |
| 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 | 0.001 |
| 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".