The Biomechanics and Optimization of the Needle-Syringe System for Injecting Triamcinolone Acetonide into Keloids
Bibliographic record
Abstract
Purpose. Injecting triamcinolone acetonide (TA) into a keloid is physically challenging due to the density of keloids. The purpose was to investigate the effects of various syringe and needle combinations on the injection force to determine the most ergonomic combination. Materials and Methods. A load cell was used to generate and measure the injection force. Phase 1: the injection force of 5 common syringes was measured by injecting water into air. The syringe that required the lowest injection force was evaluated with various needle gauges (25, 27, and 30 G) and lengths (16, 25, and 38 mm) by injecting TA (40 mg/mL) into air. The needle-syringe combination with the lowest injection force (CLIF) was deemed the most ergonomic combination. Phase 2: comparisons between the CLIF and a standard combination (SC) were performed by injecting TA into air and tap water into a keloid specimen. Intraclass Correlation Coefficient (ICC) and independent t -test were used. Results. Increasing the syringe caliber, injection speed, and needle gauge and length significantly increased the injection force ( p value < 0.001). The SC required a maximum force of 40.0 N to inject water into keloid, compared to 25.0 N for the CLIF. Injecting TA into keloid using the SC would require an injection force that was 103.5% of the maximum force female thumbs could exert compared to 64.8% for the CLIF. ICC values were greater than 0.4. Conclusions. The 1 mL polycarbonate syringe with a 25 G, 16 mm needle (CLIF) was the most ergonomic combination. The SC required a substantial injection force, which may represent a physical challenge for female thumbs.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".