Impact of surgery for stage IA non-small-cell lung cancer on patient quality of life
Bibliographic record
Abstract
BACKGROUND: There is a paucity of literature comparing quality of life (QoL) before and after surgery in stage IA lung cancer, where surgical resection is the recommended curative treatment. OBJECTIVE: To assess the impact of surgery on physical and mental health-related QoL in patients with stage IA lung cancer treated with surgical resection. METHODS: Participants in the I-ELCAP cohort who were diagnosed with their first primary pathologic stage IA non-small-cell lung cancer, underwent surgery, and provided follow-up information on QoL 1 year later were included in the present analysis (N = 107). QoL information was collected using the SF-12 (12-item Short Form Health Survey), which generates 2 component scores related to mental health and physical health. RESULTS: Statistical analyses indicated that physical health QoL was significantly worsened from before surgery to after surgery, whereas mental health QoL marginally improved from before to after surgery. Physical health QoL worsened for women from baseline to follow-up, but not for men. Only lobectomy (not limited resection) had an impact on QoL from before to after surgery. LIMITATIONS: Results are considered preliminary given the small sample size and multiple comparisons. CONCLUSIONS: The current study findings have implications for lung cancer health care professionals in regard to how they can most effectively present the possible impact of surgery on quality of life to this subset of patients in which disease has not yet significantly progressed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".