Canadian Expert Opinion on Breast Reconstruction Access: Strategies to Optimize Care during COVID-19
Bibliographic record
Abstract
BACKGROUND: Breast reconstructive services are medically necessary, time-sensitive procedures with meaningful health-related quality of life benefits for breast cancer survivors. The COVID-19 global pandemic has resulted in unprecedented restrictions in surgical access, including access to breast reconstructive services. A national approach is needed to guide the strategic use of resources during times of fluctuating restrictions on surgical access due to COVID-19 demands on hospital capacity. METHODS: A national team of experts were convened for critical review of healthcare needs and development of recommendations and strategies for patients seeking breast reconstruction during the pandemic. Following critical review of literature, expert discussion by teleconference meetings, and evidenced-based consensus, best practice recommendations were developed to guide national provision of breast reconstructive services. RESULTS: Recommendations include strategic use of multidisciplinary teams for patient selection and triage with centralized coordinated use of alternate treatment plans during times of resource restrictions. With shared decision-making, patient-centered shifting and consolidation of resources facilitate efficient allocation. Targeted application of perioperative management strategies and surgical treatment plans maximize the provision of breast reconstructive services. CONCLUSIONS: A unified national approach to strategically reorganize healthcare delivery is feasible to uphold standards of patient-centered care for patients interested in breast reconstruction.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".