Scoping clinicians’ perspectives on pre-treatment multidisciplinary care for young women with breast cancer
Bibliographic record
Abstract
BACKGROUND: Young women with breast cancer (YWBC) experience worse medical and psychosocial outcomes than their older counterparts. Early input from a multidisciplinary team via pre-treatment multidisciplinary cancer conferences (pMCCs) may be important for addressing the complex needs of YWBC. However, pMCCs are not common. This study has two parts: a survey and workshop aimed at assessing clinicians' perspectives on pMCCs, including the importance of pMCCs in the care of YWBC, as well as barriers to, and strategies for supporting their implementation. METHODS: Survey results highlight variability across sites in the delivery of multidisciplinary care in general. However, both survey and workshop results emphasize clinicians' agreement on the importance of pMCCs and suggest that numerous practical and systems levels barriers be addressed before pMCCs can be implemented. CONCLUSIONS: pMCCs have the potential to improve surgical treatment and psychosocial outcomes for YWBC. A combined practical and policy approach to their implementation, which sees extension of existing standards to include pMCCs, may support their adoption and subsequent audit practices to assess the effect of pMCCs on outcomes for YWBC.
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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 | 0.016 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".