A Computed Tomographic Investigation of the Ophthalmic Artery Volume and Its Relevance to Soft Tissue Filler Injections
Bibliographic record
Abstract
BACKGROUND: The measured intraarterial volume of cadaveric ophthalmic arteries was utilized for safety recommendations during facial soft tissue filler injections. However, its clinical practicability and model applicability have become questionable. OBJECTIVES: To measure the volume of the ophthalmic artery in living individuals by utilizing computed tomography (CT) imaging technology. METHODS: A total of 40 Chinese patients (23 males, 17 females) were included in this study with a mean age of 61.0 (14.2) years and a mean body mass index of 23.7 (3.3) kg/m2. Patients were investigated with CT imaging technology to evaluate the length, diameter, and volume of the bilateral ophthalmic arteries as well as the length of the bony orbits, resulting in a total of 80 investigated ophthalmic arteries and orbits. RESULTS: Independent of gender, the average length of the ophthalmic artery was 80.6 (18.7) mm, the calculated volume of the ophthalmic artery was 0.16 (0.05) mL and the minimal and maximal internal diameter of the ophthalmic artery were 0.50 (0.05) mm and 1.06 (0.1) mm, respectively. CONCLUSIONS: Based on the results obtained from the investigation of 80 ophthalmic arteries it must be concluded that current safety recommendations should be reevaluated. The volume of the ophthalmic artery appears to be 0.2 mL rather than 0.1 mL as previously reported. In addition, it appears impractical to limit the volume of soft tissue filler bolus injections to 0.1 mL due to the aesthetic requirements of each individual patient and treatment plan.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".