Identification of the anatomy of the deep temporal vein using computed tomography imaging: A retrospective cross‐sectional review of patient imaging
Bibliographic record
Abstract
PURPOSE: The deep temporal vein (DTV) can be used in free flap procedures when the superficial temporal vein is inadequate. Despite its potential utility, its branching patterns have only been examined in one small anatomic study. The purpose of this study was to examine computed tomography angiography (CTA) images to determine DTV location, variation, and suitability as a microvascular recipient, to provide surgeons with a guide for its use in head and neck defects. METHODS: A retrospective chart review identified 152 patient CTA images (76 female; 76 male) in a single academic center imaging database, selected consecutively from January 2017 to April 2020. Patients under 19 years were excluded; ages ranged from 19 to 80 years (average 51.6 years). Reason for imaging, DTV caliber, laterality, distance to zygomatic arch (ZA [coronal and sagittal]), distance to lateral orbital rim (LOR), and branching pattern were recorded. RESULTS: The predominant reason for imaging was to rule out cerebrovascular accident (96.2%). Average caliber was 3.46 ± 1.29 mm (95% confidence interval [CI] [3.32, 3.61]; range, 1.00-10.8). Bilateral DTVs were observed in 98.7% of patients. Average distance to landmarks were as follows: ZA (coronal), 13.8 ± 5.85 mm (95% CI [13.2, 14.5]; range, 2.7-33.8); ZA (sagittal), 15.1 ± 6.12 mm (95% CI [14.1, 16.1]; range, 2.8-47.2); LOR, 47.1 ± 9.09 mm (95% CI [46.0, 48.1]; range, 10.8-62.9). Seven branching patterns were identified, including a posterior vertical variant that bypasses the superficial temporal fat pad. CONCLUSIONS: The DTV is a "lifeboat" option for head and neck reconstruction. Its average caliber is sufficient for use in microsurgery. Knowledge of both its typical and aberrant courses allow for efficient preoperative planning and surgical dissection. CTA is a useful adjunct when planning to use the DTV for free tissue transfer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".