Evaluating A Multidisciplinary Cancer Conference Checklist: Practice Versus Perceptions
Bibliographic record
Abstract
Arden L Corter,1 Brittany Speller,1 Kristin McBain,2 Frances C Wright,3,4 May Lynn Quan,5 Erin Kennedy,6 Selina Schmocker,6 Nancy N Baxter1,4,7On behalf of the RUBY Cohort Investigators1Department of Surgery, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Canada; 2Applied Health Research Centre, St Michael’s Hospital, Toronto, Canada; 3Department of Surgery, Sunnybrook Hospital, Toronto, ON M4N 3M5, Canada; 4Division of General Surgery, Department of Surgery, University of Toronto, Toronto, Canada; 5Department of Surgery, University of Calgary, Foothills Medical Centre, Calgary, AB T2N 2T9, Canada; 6Division of General Surgery, Mount Sinai Hospital, Toronto, Canada; 7Dalla Lana School of Public Health, University of Toronto, Toronto, CanadaCorrespondence: Nancy N BaxterDepartment of Surgery, St. Michael’s Hospital, 16-040 Cardinal Carter Wing, 30 Bond Street, Toronto, Ontario M5B1W8, CanadaTel +1416 864-5168Fax +1416 360-0637Email nancy.baxter@unityhealth.toBackground: 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.Keywords: breast neoplasm, multidisciplinary, cancer conference, implementation, evaluation, checklist
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 | 0.141 | 0.416 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".