Clinical evaluation of silicone gel in the treatment of cleft lip scars
Bibliographic record
Abstract
Upper lip scars are at risk of hypertrophy. Our center therefore uses microporous tape and silicone sheeting for postoperative scar care following cleft lip repair. However, some babies have previously ingested their silicone sheeting, which has the potential for respiratory compromise or gastrointestinal obstruction. Self-dry silicone gel is reportedly also effective for preventing hypertrophic scars. Hence, we sought to test whether silicone gel, which cannot be ingested whole, might be non-inferior to silicone sheeting for controlling against upper lip scar hypertrophy. This was a mixed prospective and retrospective case-controlled clinical trial involving patients undergoing unilateral cleft lip repair, 29 of whom received standard postoperative silicone sheeting (control group) and another 33 age-matched consecutive patients who received self-dry silicone instead. The Vancouver scar scale, visual analogue scale and photographically assessed scar width assessments were the same in both groups at six months after surgery. In conclusion, silicone gel appears to be non-inferior to silicone sheeting for postoperative care of upper lip scars as judged by scar quality at six months, but silicone sheeting has the safety disadvantage that it can be swallowed whole by babies. It is thus recommended that silicone gel be used for upper lip scar management in babies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".