A Review of Objective Measurement of Flap Volume in Reconstructive Surgery
Bibliographic record
Abstract
BACKGROUND: The utility and efficacy of 3-dimensional representation have been proven in bony reconstruction; however, its role in soft-tissue reconstruction remains limited. There is currently no reliable gold standard to objectively measure flap volume. This systematic review aims to summarize the available techniques used to objectively measure flap volume in reconstructive surgery. METHODS: A systematic literature search was performed to identify all relevant articles describing objective techniques to quantify flap volume. The search included published articles in 3 electronic databases-Ovid MEDLINE, EMBASE, and PubMed. RESULTS: A total of 16 studies were included. Flap volume was calculated using the following techniques: magnetic resonance imaging, computed tomography, 3-dimensional imaging and modeling, material templates, ultrasound, and weighing scales. Techniques and results of the included studies are summarized. CONCLUSIONS: This systematic review provides a summary of various published techniques for objective pre- or intraoperative quantification of flap volume in reconstructive surgery. The preliminary results from this review are promising, and we believe that 3-dimensional representation and objective quantification is the future of reconstructive flap surgery. More studies are needed to study the clinical relevancy and impact of the various imaging modalities reviewed and to develop automated volumetric measurement technology with improved accuracy, efficacy, and reproducibility.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".