Evaluation of physician volumetric accuracy during hyaluronic acid gel injections: An observational, proof‐of‐concept study
Bibliographic record
Abstract
BACKGROUND: Findings indicate that standard injection techniques for hyaluronic acid (HA) soft tissue fillers result in considerable variations in the applied boluses. Furthermore, despite the knowledge that the varying biophysical properties of HA fillers (eg, G', cohesivity, viscoelasticity) can affect their ease of injection, the impact of device attributes on an injector's volumetric accuracy is currently unknown. OBJECTIVES: (a) Evaluate the ability of aesthetic physicians to accurately inject a specific amount of HA filler; (b) investigate the effect of physician experience on injection accuracy; and (c) investigate the impact of different HA gel properties on an injector's performance. METHODS: Thirteen physicians with aesthetic experience were recruited. Subjects were blindfolded and asked to deposit 0.2 cc of four HA fillers under two conditions: (a) onto a scale and (b) into a porcine membrane. The amount of gel deposited/injected was then measured. An accurate injection was defined as 0.2 cc ± 15%. RESULTS: Subjects were rarely able to dispense accurate amounts of injectate (42/208 injections or 20.19%) and often underestimated their injection quantities. Variations in the accuracy distributions between groups were observed. An injector's experience, the conditions under which injections were performed and the product choice were all variables found to affect the accuracy distributions. CONCLUSIONS: The findings of this study provide evidence that the use of visual and/or audible stimuli is necessary for dispensing accurate volumes. This has clinical implications for treatment efficacy and patient satisfaction during free hand injections. To ensure more accurate and reproducible results, the investigators propose a possible modification to the standard injection technique.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".