Impact of Multidisciplinary Team Care on Patient-Reported Outcomes in Patients with Lung Cancer: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Multidisciplinary team (MDT) care is now recognized as the most effective approach to managing lung cancer treatment. While MDTs aim to improve coordination, decision-making, and patient outcomes, their impact on patient-reported outcomes, particularly quality of life (QoL), remains unclear. OBJECTIVE: This systematic review aimed to examine how the involvement of a multidisciplinary team (MDT) in the care of patients with lung cancer affects patient-reported outcomes and to investigate the enablers and barriers for implementing and running MDT care in lung cancer management. METHODS: We systematically searched Medline, Embase, Cochrane, and Scopus (up to March 2024) to identify studies comparing QoL outcomes in patients with lung cancer managed with and without MDT care. The review was conducted and reported in accordance with the PRISMA 2020 guidelines. Risk of bias was assessed using the CASP tool, and findings were synthesized narratively. QoL outcomes were grouped into physical, functional, emotional, and social domains, and quantitative and qualitative data were synthesized narratively due to heterogeneity across studies. RESULTS: Eleven studies met the inclusion criteria, comprising a total of 10,341 patients, with 3760 in MDT groups and 6581 in non-MDT groups. The methodological quality of the studies varied, with 10 papers rated as moderate to high quality. The findings suggest that MDT care may contribute positively to emotional support, and physical well-being. Better patient satisfaction and communication in MDT settings. LIMITATION: Heterogeneity and the lack of standardized PRO tools in outcome measures and study design limited comparability. CONCLUSIONS: MDT care may have a beneficial impact on certain aspects of quality of life in patients with lung cancer, particularly emotional and physical well-being. However, more robust and standardized research is needed to determine the full extent of its benefits on patient-reported outcomes.
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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 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".