What decision making tools are available when deciding on breast reconstruction? A Scoping Review Protocol
Bibliographic record
Abstract
Breast cancer is the most common cancer among Canadian women. Total mastectomy may be an important component of breast cancer treatment and, for many women, may include breast reconstruction. Reconstruction of the breast mound can be performed at the time of ablative surgery, known as immediate breast reconstruction (IBR). The time between a patient’s breast cancer diagnosis and their mastectomy with or without IBR is quite short, such that many patients are left dissatisfied and without enough understanding to make an informed decision following their surgical consultation. Another barrier within this process is that many patients may not know how to engage in the conversation and ask the questions they want to hear answers to. Question prompt lists and decision-making tools have shown to help patients with this issue. The aim of this scoping review is to determine what literature and resources exist on checklists and decision-making tools for post-mastectomy patients undergoing breast reconstruction both within academic and non-academic mediums. We will examine what kind of resources exist for this population, how accessible they are, which platforms they’re published on and their efficacy.
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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 | 0.109 | 0.127 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.070 | 0.010 |
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".