Systematic review on shared decision making for patients with lung cancer: Effects on distress and health care utilization.
Bibliographic record
Abstract
31 Background: Lung cancer is associated with significant distress, poor quality of life, and a median prognosis of less than one year. Shared decision making (SDM) has been recommended as a strategy to help guide patients facing difficult treatment trade-offs. Potential benefits of SDM include enhanced knowledge and better congruence between treatment decisions and patients’ personal values and have been described in multiple diseases. We investigated the impact of SDM on distress and healthcare utilization among patients with lung cancer. Methods: We performed a systematic literature search in the CINAHL, Cochrane, EMBASE, MEDLINE, and PsychINFO databases. Studies were eligible when conducted among patients with lung cancer, evaluated SDM, and measured distress and/or health care utilization as outcomes. Risk of bias was assessed using the Cochrane risk of bias tool. Results: A total of 11 articles were identified: two retrospective cohort studies and nine articles reporting on eight randomized controlled trials. Overall, the risk of bias of included studies was low, except for a high risk of bias concerning blinding of participants or personnel. All studies reported on a broad supportive care intervention with SDM as a component of the intervention. No beneficial effect was found in five studies measuring generic distress, while one study reported beneficial effects on depression. There was conflicting evidence regarding the effects of SDM on healthcare utilization; of the seven studies analyzing this, five studies found evidence for a reduction in healthcare utilization. Conclusions: Although relevant, only scarce evidence is currently available on the effects of SDM on distress and healthcare utilization among patients with lung cancer. Thus, additional research is needed before SDM can be recommended in the lung cancer context.
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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 | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".