Algorithm for Using a Long-Pulsed Nd
Bibliographic record
Abstract
BACKGROUND Conventional therapies for deep cutaneous vascular anomalies have demonstrated poor efficacy and many side effects. New laser systems offer greater potential to treat these difficult lesions, but the lack of specific treatment guidelines has restricted consistent success. OBJECTIVE To establish a rational, user-friendly algorithm that incorporates basic components of deep vascular lesions to define the correct laser settings required for safe, effective, and reproducible treatment. METHODS Within 18 months, 162 deep vascular lesions of various types and anatomic sites were evaluated for vessel size, depth, color, and pressure. An algorithm incorporating these characteristics was employed to determine laser parameter settings. Using a high-peak power, long-pulse 1064-nm Nd:YAG laser system, the vascular lesions were then treated. RESULTS Within 6 months of follow-up, 80% of treated areas demonstrated a 50% or greater resolution after a single treatment session, with complete clearance shown in 19%. Only minimal and transient side effects were observed. Of note, 74% of areas on the extremities and 83% within the oral cavity showed a 50% or greater resolution after one treatment. CONCLUSION Previously challenging deep cutaneous vascular anomalies may be safely reduced or cleared with the use of an appropriate laser system and this algorithm-directed technique. This represents a significant breakthrough in the management of vascular lesions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".