Identifying the top research priorities in postmastectomy breast cancer reconstruction: a James Lind Alliance priority setting partnership
Bibliographic record
Abstract
BACKGROUND: Major improvements in breast cancer treatment in the last decade include advancements in postmastectomy breast reconstruction (PMBR). Unfortunately, the studies in PMBR are primarily researcher or industry led with minimal input from patients and caregivers. The aim of this study is to use the James Lind Alliance (JLA) approach to bring together the patients, caregivers and clinicians in a priority setting partnership to identify the most important unanswered research questions in PMBR. METHODS: The JLA priority setting methodology involved four key stages: gathering research questions on PMBR from patients, caregivers and clinicians; checking these research questions against existing evidence; interim prioritisation and a final consensus meeting to determine the top 10 unanswered research questions using the modified nominal group methodology. RESULTS: In stage 1, 3168 research questions were submitted from 713 respondents across Canada, of which 73% of the participants were patients or caregivers. Stage 2 confirmed that there were a total of 48 unique unanswered questions. In stage three, 488 individuals completed the interim prioritisation survey and the top 25 questions were taken to a final consensus meeting. In the final stage, the top 10 unanswered research questions were determined. They cover a breadth of topics including personalised surgical treatment, safety of implants and newer techniques, access to PMBR, breast cancer recurrence and rehabilitation. INTERPRETATION: Identification of the top 10 unanswered research questions is an important first step to generating relevant and impactful research that will ultimately improve the PMBR experience for patients with breast cancer.
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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 | 0.346 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.005 | 0.042 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".