Is constant needle motion during soft tissue filler injections a safer procedure? A theoretical mathematical model for evaluating patient safety
Bibliographic record
Abstract
BACKGROUND: The safety rationale behind the constant needle motion injection technique is based on the assumption that due to the constant needle motion and simultaneous soft tissue filler material administration a smaller amount of product per area may be injected into an artery if an artery within the range of the moving needle is inadvertently entered. OBJECTIVE: To perform mathematical calculations for determining the probability for causing intra-arterial product administration when constantly moving the needle during facial aesthetic soft tissue filler injections. METHODS: This study was designed as a theoretical investigation into the probabilities for causing adverse events due to intravascular injection of soft tissue filler material when constantly moving a 27-G needle during facial soft tissue filler administration. RESULTS: It was revealed that with a higher number of conducted injection passes a greater soft tissue area can be covered by the needle. The odds of encountering an artery within the covered soft tissue volume and the odds of injecting any volume greater than zero into the arterial blood stream increases with the number of performed injection passes. This increase is greatest between 1 and 10 performed injection passes. CONCLUSION: This model demonstrates that the constant needle motion technique increases the probability of encountering an artery within the treatment area and thus increases the odds for intra-arterial product administration. The constant needle motion technique does not increase safety but rather may increase the odds of causing intra-arterial product administration with the respective adverse consequences for the patient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".