Systematic Review for the Follow-up of Curatively Treated Patients With Lung Cancer
Bibliographic record
Abstract
INTRODUCTION: The follow-up of patients with lung cancer after curative-intent treatment should include strategies to improve their quality of life and survival. These could include the monitoring and management of symptoms of recurrence and late toxicities from cancer treatments, the use of patient-reported outcome (PRO) tools, and smoking cessation interventions. The objective of this systematic review was to examine these follow-up strategies. MATERIALS AND METHODS: This systematic review was developed by Ontario Health (Cancer Care Ontario)'s Program in Evidence-Based Care. MEDLINE, EMBASE, and the Cochrane Library were searched for systematic reviews and randomized controlled trials (RCTs) comparing different types of clinicians, PRO tools, smoking cessation interventions, and management strategies for signs, symptoms, risk factors, comorbidities, or late toxicities in adult patients with NSCLC or SCLC after curative-intent treatment. RESULTS: Thirty-five RCTs and nineteen systematic reviews were included. For nurse-led interventions, either significant effects were found in favor of the intervention, or no significant effects were found. The results for the use of PRO tools were mixed, possibly due to differences in comparators and settings. Evidence suggested that smoking cessation interventions might benefit these patients (RR, 0.84; 95% CI, 0.68-1.03). There was limited evidence in the target population for the management of signs, symptoms, risk factors, comorbidities, or late toxicities. CONCLUSIONS: Smoking cessation interventions, exercise training, and the use of PRO tools may benefit these patients. The results of this systematic review were used to inform recommendations in a clinical practice guideline. Further RCTs in this patient population are needed.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".