Feasibility of Implementing a Breast Reconstruction Database
Bibliographic record
Abstract
OBJECTIVE: To assess whether implementing a breast reconstruction database would be feasible in terms of time commitment, cost, and overall benefits in a tertiary-care hospital. METHODS: A survey was sent to 40 Canadian plastic surgeons who have a practice focused on breast reconstruction. The survey assessed demographics, practice characteristics, database use, and opinions on database construction. Univariate descriptive analyses were performed on all variables. RESULTS: Thirty-one surgeons responded to the survey (77.5%). Most were from Ontario (29.1%) and worked in an academic center (83.9%). Of all, 45.3% of surgeons performed more than 50 breast reconstructions yearly. Six (19.4%) surgeons utilized databases that were all started for quality improvement and research purposes. Databases included variables such as demographics, type of reconstruction, complications, surgeons involved, and type of implants. Data are input by research assistants (50%) for approximately 4.2 hours per month at a cost below 200$CAD per month. Databases are funded by research grants (50%), hospital funds (33.3%), and/or division funds (16.7%). Of the surgeons without databases, 60% have considered starting a database. Barriers include being too busy (72%) and impressions of the cost being too high (32%). Surgeons commonly felt that a database would be beneficial at their practice (80%), provincially (77.4%), and nationally (67.7%). CONCLUSIONS: Plastic surgeons are open to the idea of constructing a breast reconstruction database and that the costs and time required are lower than expected. Grants or integration with existing databases should be pursued on a provincial level first prior to pursuing a national database.
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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 | 0.019 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".