Refining Perforator Selection for DIEP Breast Reconstruction Using Transit Time Flow Volume Measurements
Bibliographic record
Abstract
Transit time flow volume has been used in cardiac surgery to assess small vessel flow characteristics. This study examines the usefulness of transit time flow volume (TTFV) in assessing perforator vessels in deep inferior epigastric artery perforator (DIEP) flap harvesting. The purpose of this study was to evaluate the correlation among computed tomographic angiography (CTA), intraoperative TTFV measurements, and hand-held Doppler signals in identifying perforators. Ten consecutive free DIEP breast reconstructions were prospectively evaluated using CTA to identify abdominal wall perforators. Intraoperatively, perforating vessels >1 mm in diameter were evaluated with a conventional hand-held 8-MHz Doppler and a TTFV measurement device. Vessel location was correlated with preoperative CTA . Waveform patterns and TTFV measurements were recorded for each vessel and correlated with both CTA and hand-held Doppler signals. Of the 54 perforators identified, TTFV showed arterial flow waveforms in 15 of 16 perforators identified by CTA and in 2 of the remaining 38 vessels. The sensitivity and specificity of TTFV in identifying arterial perforators were 94 and 95%, respectively. In contradistinction, hand-held Doppler was misleading in 70% of vessels. TTFV distinguishes arterial from venous waveforms in vessels that appear arterial by hand-held Doppler signals. CTA and TTFV are highly correlated, and the use of TTFV may prevent poor perfusion seen in some DIEP flaps.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".