Changes in quality of life of early-stage lung cancer patients undergoing sublobar resection: a systematic review
Bibliographic record
Abstract
Objective: This systematic review aimed to evaluate the impact of sublobar resection (SLR) on the quality of life (QoL) of patients with early-stage non-small cell lung cancer (NSCLC). Specifically, it compared outcomes between sublobar resection, lobectomy, and stereotactic body radiation therapy (SBRT). Methods: A literature search was conducted across PubMed and Scopus, identifying studies published from 2010 to 2024 that reported QOL outcomes in early-stage NSCLC patients treated with lobectomy, SLR, or SBRT. Inclusion criteria were studies with more than 10 patients, written in English, and using validated QoL metrics. Data on demographics, interventions, QoL tools, and findings were extracted, and study quality was assessed using the Newcastle-Ottawa Scale and the ROBINS-I tool. Results: Five studies involving 1,149 patients from six countries met the inclusion criteria. QoL outcomes consistently favored SLR over lobectomy in domains such as physical and respiratory function, with SLR patients experiencing faster recovery and fewer complications. Minimally invasive techniques, such as video-assisted thoracoscopic surgery (VATS), further enhanced these outcomes. SBRT demonstrated stable QOL post-treatment but lacked the long-term physical recovery benefits observed with SLR. Commonly employed QoL tools included the EORTC QLQ-C30, Leicester Cough Questionnaire, and NSCLC-PQOL, each capturing distinct dimensions of patient QoL status. Conclusion: Sublobar resection provides significant QoL benefits for selected early-stage NSCLC patients compared to lobectomy, particularly in respiratory health and recovery endpoints. These findings highlight the value of personalized surgical approaches and the need for further research on optimizing QoL in NSCLC management.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".