Histological findings correlated with clinical outcomes in telangiectasia treated with ohmic thermolysis and 940 nm laser
Bibliographic record
Abstract
BACKGROUND: Heat modalities are commonly used as either primary or adjunctive treatment for telangiectasia. Minimal information is available as to the nature of injury to the vessel and surrounding tissue. METHOD: A total of 135 patients were treated over a 2-year period using ohmic thermolysis (45), 940 nm laser (50), and 940 nm laser with sclerotherapy (40). After treatment, 1 mm biopsies were done in selected patients in each group. Clinical correlation was studied in each group by observing vessel response at 4-6 weeks postprocedure. RESULTS: Ohmic thermolysis produces electrodessication of the squamous epithelium, reticular dermis, and fusion of the target vessel. 940 nm laser results include squamous epithelial damage, subcutaneous water blister, collagen denaturation, and vessel endothelial cell loss with thrombus at point of maximal impact. The addition of sclerotherapy at time of laser potentiates vessel damage. There was no long-term skin sequelae after treatment when each device is used at recommended settings and on appropriate vessel size. CONCLUSION: Each device causes damage to the squamous epithelium and papillary reticular dermis that is transient. Ohmic thermolysis provides vessel clearance of >90% in telangiectasias <0.5 mm. 940 nm laser effectiveness is <70% for vessel clearance, but improves to >90% when sclerotherapy is performed at time of treatment.
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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 | 0.000 |
| 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".