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 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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".