Predictive value of 3D imaging to guide implant selection in immediate breast reconstruction
Bibliographic record
Abstract
BACKGROUND: Pre-operative estimation of breast mound volume for immediate breast reconstruction is necessary for operative planning, especially in direct-to-implant reconstruction. Our purpose was to investigate the relationship between pre-operative predictions of breast mound weight from 3D imaging and actual mastectomy weight and implant size. METHODS: A retrospective chart review of all patients who had previously undergone nipple-sparing mastectomy (NSM) by a single surgeon was performed. Pre-operative 3D images were reviewed and calculations of breast mound weight were performed by three independent reviewers. Intra-operative mastectomy weight and final implant weight were collected from patient charts. A regression analysis between calculated and actual values was performed. RESULTS: There were 59 reconstructed breasts included. Pre-operative 3D imaging-guided breast weight calculations were similar across reviewers (R=0.96). Pre-operative calculations of breast weight were 49.4g (SD=134.0) smaller than actual mastectomy specimens. Mastectomy specimens were 41.0g (SD=130.2) smaller than final implant sizes. Thereby, the relationship was as follows: Pre-operative calculated breast weight < actual Mastectomy weight < implant weight. Mastectomy weight and final implant size had linear relationships with pre-operative calculations of breast weight. Formulas for predicting mastectomy weight [mastectomy weight = 63.2 + 0.95 (pre-operative calculated weight)] and implant size [Implant weight = 209.7+ 0.56 (pre-operative calculated weight)] from pre-operative calculations of breast weight were generated. CONCLUSIONS: Three-dimensional scanning technologies may be a useful tool to predict implant sizes for direct-to-implant breast reconstruction. Final implant size was heavier than intra-operative mastectomy weight and pre-operative calculated breast mound weight.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".