Evaluation of Cannula Safety in Injection of Poly-L-Lactic Acid
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Poly-L-lactic acid (PLLA) has been used in various medical applications for decades, including aesthetic ones. The use of a cannula technique in injecting PLLA has been proposed in order to lower the incidence rate of adverse events (AEs) following treatment. Such AEs include nodule formation, which may occur less frequently by fanning the product with a cannula, thus creating a more uniform product placement compared to that resulting from the use of a needle. Currently, however, there is a lack of comparative research regarding the safety of cannulas versus needles for PLLA injections, as the selection of either remains highly subjective. Therefore, the objective of our study was to investigate the safety of cannula use in the administration of PLLA, in order to report safety outcomes. MATERIALS AND METHODS: Aesthetic™ in the face and/or neck regions. Twenty-seven subject charts met eligibility. Descriptive data regarding treatment and follow-up visits were collected and analyzed. RESULTS: A total of seven AEs resulted from eighty-two treatment sessions (8.54%), with 6/27 patients having experienced at least one AE (22.22%). Mild bruising was the most commonly reported AE (57.14%). The majority of the AEs were mild and transient in nature, with one moderate AE being a nodule that was possibly related to a concomitant treatment. All AEs were resolved with follow-up care. CONCLUSION: Mild AEs such as bruising, swelling and pain should be expected following the use of a cannula for PLLA injections. However, the incidence rates of AEs following treatment can remain low if proper product preparation and treatment techniques are utilized.
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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 | 0.003 | 0.007 |
| 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".