Review: non-invasive interventions improve symptoms and psychological functioning in patients with lung cancer
Bibliographic record
Abstract
Solà I, Thompson E, Subirana M, et al . Non-invasive interventions for improving well-being and quality of life in patients with lung cancer. Cochrane Database Syst Rev 2004;(4):CD004282. Q Are non-invasive interventions delivered by healthcare professionals effective for improving symptoms, psychological functioning, and quality of life in patients with lung cancer? ### ![Graphic][1] Data sources: Cochrane Central Register of Controlled Trials ( Cochrane Library, Issue 4, 2003), Medline (1966–2003), EMBASE/Excerpta Medica (1974–2003), CINAHL (1982–2002), CancerLit (1975–2002), PsycINFO (1873–2003), bibliographies of relevant articles, and researchers in the field. ### ![Graphic][2] Study selection and assessment: randomised controlled trials (RCTs) or controlled clinical trials (CCT) that evaluated non-invasive interventions (ie, any physical treatment that does not require catheterisation, skin puncture, intubation, incision, drainage, endoscopy, or pharmacological intervention) based on clinical judgment and knowledge, and performed by healthcare professionals to enhance patient wellbeing or quality of life in patients with lung cancer (patients with other … [1]: /embed/inline-graphic-1.gif [2]: /embed/inline-graphic-2.gif
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".