<p>Evaluating A Multidisciplinary Cancer Conference Checklist: Practice Versus Perceptions</p>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Presentation to multidisciplinary cancer conferences (MCCs) supports optimal treatment of young women with breast cancer (YWBC). However, research shows barriers to MCC practice, and variation in professional attendance and referral patterns. A checklist may help overcome these barriers and support MCC practice with YWBC. METHODS: We developed, piloted and evaluated an MCC checklist in sites participating in a pan-Canadian study (RUBY; Reducing the bUrden of Breast cancer in Young women). A survey assessed checklist processes and impacts, and checklist data were analysed for checklist uptake, MCC presentation rates and MCC processes including staff attendance. RESULTS: Fifteen RUBY sites used the checklist (~50%), mostly for data collection/tracking. Some positive effects on clinical practice such as increased presentation of YWBC at MCC were reported, but most survey participants indicated that MCC processes were sufficient without the checklist. Conversely, checklist data show that only 31% of patients were presented at MCC. Of those, 41% were recommended treatment change. CONCLUSION: Despite limited checklist uptake, there was evidence of its clinical practice benefit. Furthermore, it supported data collection/quality monitoring. Critically, checklist data showed gaps in MCC practice and low MCC presentation rates for YWBC. This contrasts with overall provider perceptions that MCCs are working well. Findings suggest that supports for MCC are needed but may best take the form of clear national practice recommendations and audit and feedback cycles to inform awareness of good MCC practice and outcomes. In this setting, tools like the MCC checklist may become helpful in supporting MCC practice.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it