[Verapamil in conjunction with pressure therapy in the treatment of pathologic scar due burn injury].
Bibliographic record
Abstract
BACKGROUND: Keloids and hypertrophic scars are dermal fibro-proliferative disorders unique to humans. Their treatment is a true challenge with multiple options, but not all the time with good results. Unfortunately this problem is not uncommon in patients with history of burn injury. The aim of this article is to evaluate the use of verapamil and pressure garments in patients with hypertrophic or keloid scar caused by burn injury. METHODS: We included patients with a hypertrophic or keloid scar caused by burn injury candidate to treatment with pressure garment. The pathologic scars were evaluated by serial photographic records, Vancouver and Posas scales. The scales of Vancouver and Posas were compared with t Student. RESULTS: We included 13 scars in 11 patients. Four scars were located in the legs, 4 in the arms, 4 in the face-neck and 1 in the abdomen. The dose of verapamil was calculated .03mg per kg. Injections were scheduled every 7 to 10 days until complete 6 sessions. Taking in count Posas scale, patients referred improvement in pigmentation (.01), thickness (.005), pliability (.01) and surface area (.004). In the Vancouver scale the observers mentioned improvement in elevation (.008), pigmentation (.014), vascularity (.022), flexibility (.014) and pruritus (.003). No adverse effects were found in verapamil injection. CONCLUSIONS: Verapamil was useful in conjunction with pressure garment to improve the condition of the keloid and hypertrophic scar caused by burn.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".