Spring-Powered Needle-Free Injection of Triamcinolone Acetonide and 5-Fluorouracil for Keloid Treatment
Bibliographic record
Abstract
Introduction: Keloid is an abnormal fibroproliferative healing response characterized by excessive and invasive tissue growth beyond the wound boundaries. The conventional treatment involves injecting drugs such as triamcinolone acetonide (TA), 5-fluorouracil (5-FU), or their combination intralesionally. However, the pain associated with injections often leads to low patient compliance and treatment failure. The spring-powered needle-free injector (NFI) provides an affordable alternative option for drug delivery with reduced pain. Case: This case report presents a 69-year-old female patient with a keloid treated using a spring-powered needle-free injector (NFI) for drug delivery. The keloid was assessed using the Vancouver Scar Scale (VSS) and the Patient and Observer Scar Assessment Scale (POSAS). The patient’s pain level was measured using the Numeric Pain Rating Scale (NPRS). TA and 5-FU mixed with lidocaine were loaded into the NFI and injected at a dose of 0.1 mL/cm 2 . The treatment was repeated twice a week. After four sessions, the keloid flattened by 0.5 cm, VSS score decreased from 11 to 10, and POSAS scores decreased from 49 to 43 (observer) and from 50 to 37 (patient). The NPRS during each procedure was 1, indicating minimal pain. Discussion: The spring-powered NFI is a simple and cost-effective device that operates based on Hooke’s law, producing a high-pressure fluid jet for effective skin penetration. The NFI demonstrated effectiveness in treating keloid lesions, resulting in visible improvement after four treatments. Conclusion: The spring-powered NFI offers an affordable and painless alternative to keloid treatment. Keywords: 5-fluorouracil, keloid treatment, needle-free injection, spring-powered, triamcinolone acetonide
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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 | 0.000 |
| 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) | 0.002 | 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".