Effect of Autologous Fat Injection on Lower Eyelid Position
Bibliographic record
Abstract
PURPOSE: To evaluate the effect of autologous periorbital fat injections on lower eyelid position. METHODS: Retrospective review of patients treated with autologous periorbital (malar/eyelid tear trough) fat injections for aesthetic purposes by a single surgeon (S.N.) between March 2007 and June 2011. The primary outcome, lower eyelid position as defined by marginal reflex distance 2 and inferior scleral show, was measured by standardizing and comparing pretreatment and posttreatment follow-up digital photos. Photographs were randomized and measured by 2 masked investigators. RESULTS: Seventy patients (5 male; mean age, 53; range, 33 to 77 years) were treated with autologous fat injections to the malar and lower eyelid tear trough for aesthetic purposes. A mean decrease in marginal reflex distance 2 of 0.5 mm in both OS and OD was found when pre- and posttreatment measurements were compared. Primary and secondary mean follow-up period was at 117 and 316 days, respectively. The effect of the autologous periorbital fat injection was not diminished (n=21) between follow-up periods. A mean change in scleral show of 0.5 mm was found when pre- and posttreatment measurements were compared. The overall mean follow-up period for scleral show was 125 days. Minor complications occurred in 7% (n=5) of patients who had postinjection subcutaneous induration. CONCLUSION: Autologous fat injections are well tolerated and have potential to be an effective adjuvant or primary treatment for mild lower eyelid retraction.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".