Evaluation of three-dimensional computed tomography processing for deep inferior epigastric perforator flap breast reconstruction
Bibliographic record
Abstract
BACKGROUND: The deep inferior epigastric perforator flap procedure has become a popular alternative for women who require breast reconstruction. One of the difficulties with this procedure is identifying perforator arteries large enough to ensure that the harvested tissue is well vascularized. Current techniques involve imaging the perforator arteries with computed tomography (CT) to produce a grid mapping the locations of the perforator arteries relative to the umbilicus. OBJECTIVES: To compare the time it takes to produce a map of the perforators using either two-dimensional (2D) or three-dimensional (3D) CT, and to see whether there is a benefit in using a 3D model. METHODS: Patient CT abdomen and pelvis scans were acquired from a GE 64-slice scanner. CT image processing was performed with the GE 3D Advantage Workstation v4.2 software. Maps of the perforators were generated both as 2D and 3D representations. Perforators within a region 5 cm rostral and 7 cm caudal to the umbilicus were measured and the times to perform these measurements using both 2D and 3D images were recorded by a stopwatch. RESULTS: Although the 3D method took longer than the 2D method (mean [+/- SD] time 1:51+/-0:35 min versus 1:08+/-0:16 min per perforator artery, respectively), producing a 3D image provides much more information than the 2D images alone. Additionally, an actual-sized 3D image can be printed out, removing the need to make measurements and producing a grid. CONCLUSIONS: Although it took less time to create a grid of the perforators using 2D axial CT scans, the 3D reconstruction of the abdomen allows the plastic surgeons to better visualize the patient's anatomy and has definite clinical utility.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".