Evaluating the reliability of a tool to measure the quality of gastrointestinal multidisciplinary cancer conferences: A generalizability study
Bibliographic record
Abstract
Background Lamb et al. developed the metric for the observation of decision-making tool (MTB-MODe) to evaluate the quality of urologic multidisciplinary cancer conferences (MCCs) in the United Kingdom. We used generalizability theory to assess the reliability of a modified version of MTB-MODe in a North American context. Specifically, we wished to determine if the tool could distinguish between high- and low-quality MCC decision-making. Methods Two assessors independently evaluated two MCCs (MCC1, MCC2) using the modified MTB-MODe. Generalizability theory was used to assess overall tool reliability and to identify sources most likely to contribute to variance in reliability scores. A total of 60 cases were evaluated. Results The overall reliability scores of MCC1 and MCC2 were 0.72 and 0.74, respectively. Inter-rater reliability scores were reasonable (>0.55) and raters did not contribute significantly to variance in reliability scores. Internal consistency of the individual MTB-MODe items was low, demonstrating that items were not highly correlated. Conclusions The MTB-MODe reliably assessed the quality of individual MCC cases. Raters did not contribute significantly to reliability scores, suggesting that the tool can be successfully implemented using a single rater. Low internal consistency of the MTB-MODe items demonstrates that the tool can be used to provide feedback on individual tool items. Such data can be used by stakeholders to help improve MCC quality.
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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 | 0.193 | 0.317 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".