A new protocol (THIS and FAT) for the treatment of filler-induced vascular occlusion: a case series
Bibliographic record
Abstract
Introduction: Hyaluronic acid (HA) fillers are generally safe; however, the most significant complication is vascular occlusion. Several therapeutic protocols have been proposed for managing ischemia. De Lorenzi introduced the HDPH protocol, which uses a minimum of 500 IU of hyaluronidase (HYAL) per ischemic area. Materials and methods: This case series study evaluates the efficacy of a novel protocol, "THIS and FAT," for managing ischemia resulting from filler-induced vascular occlusion (FIVO). The protocol builds on elements from previous approaches while introducing combination therapies specifically tailored to address ischemia. The therapeutic regimen includes T: botulinum toxin type A (BTX-A), H: high-dose HYAL, I: injectable platelet-rich fibrin (iPRF), S: serum platelet-rich fibrin (sPRF), a: aspirin and antibiotics, n: nanofat, d: debridement and dermabrasion, and F: fat membrane application. Results: A total of 25 eligible patients, including 20 women and 5 men with a mean age of 32.36 ± 6.71 years, were included. The THIS and FAT protocol involved the injection of BTX-A and HYAL, with mean doses of 50.68 ± 60.79 Units and 5970.0 ± 2791.65 IU, respectively. Additionally, iPRF and sPRF were applied to the ischemic wound surface. Debridement was performed for ischemia classified as stage three or higher. Notably, 92% of patients treated with the THIS and FAT protocol showed complete improvement without scar formation. Conclusion: "THIS and FAT" Protocol for managing ischemia following FIVO shows promising outcomes. Additionally, wound management with fat membrane, iPRF injections, sPRF dressing, and nanofat application resulted in favorable outcomes in this case series.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".