Quality and efficacy of Multidisciplinary Team (MDT) quality assessment tools and discussion checklists: a systematic review
Bibliographic record
Abstract
BACKGROUND: MDT discussion is the gold standard for cancer care in the UK. With the incidence of cancer on the rise, demand for MDT discussion is increasing. The need for efficiency, whilst maintaining high standards, is therefore clear. Paper-based MDT quality assessment tools and discussion checklists may represent a practical method of monitoring and improving MDT practice. This reviews aims to describe and appraise these tools, as well as consider their value to quality improvement. METHODS: Medline, EMBASE and PsycInfo were searched using pre-defined terms. The PRISMA model was followed throughout. Studies were included if they described the development of a relevant tool, or if an element of the methodology further informed tool quality assessment. To investigate efficacy, studies using a tool as a method of quality improvement in MDT practice were also included. Study quality was appraised using the COSMIN risk of bias checklist or the Newcastle-Ottawa scale, depending on study type. RESULTS: The search returned 7930 results. 18 studies were included. In total 7 tools were identified. Overall, methodological quality in tool development was adequate to very good for assessed aspects of validity and reliability. Clinician feedback was positive. In one study, the introduction of a discussion checklist improved MDT ability to reach a decision from 82.2 to 92.7%. Improvement was also noted in the quality of information presented and the quality of teamwork. CONCLUSIONS: Several tools for assessment and guidance of MDTs are available. Although limited, current evidence indicates sufficient rigour in their development and their potential for quality improvement. TRIAL REGISTRATION: PROSPERO ID: CRD42021234326 .
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 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 | 0.128 | 0.374 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.018 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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".