Does Multidisciplinary Team Management Improve Clinical Outcomes in NSCLC? A Systematic Review With Meta-Analysis
Bibliographic record
Abstract
Introduction: The implementation of multidisciplinary teams (MDTs) has been found to be effective for improving outcomes in oncology. Nevertheless, there is still a dearth of robust literature on patients with NSCLC. The aim of this study was to conduct a systematic review regarding the impact of MDTs on patient with NSCLC outcomes. Methods: Databases were systematically searched up to February 2023. Two reviewers independently performed study selection and data extraction. Risk of bias was evaluated using the Newcastle-Ottawa and certainty of evidence by the Grading of Recommendations Assessment, Development and Evaluation approach. Overall survival was the primary outcome. Secondary outcomes included mortality, length of survival, progression-free survival, time from diagnosis to treatment, complete staging, treatment received, and adherence to guidelines. A meta-analysis with a random-effect model was performed. Statistical analysis was performed with the R 3.6.2 package. Results: = 89%). Conclusions: This meta-analysis revealed that MDT-based patient care was associated with longer overall survival and better quality-of-care-related outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.051 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.018 | 0.007 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".