Picosecond lasers: the next generation of short-pulsed lasers
Bibliographic record
Abstract
Selective photothermolysis, first discussed in the context of targeted microsurgery in 1983, proposed that the optimal parameters for specific thermal damage rely critically on the duration over which energy is delivered to the tissue. At that time, nonspecific thermal damage had been an intrinsic limitation of all commercially available lasers, despite efforts to mitigate this by a variety of compensatory cooling mechanisms. Fifteen years later, experimental picosecond lasers were first reported in the dermatological literature to demonstrate greater efficacy over their nanosecond predecessors in the context of targeted destruction of tattoo ink. Within the last 4 years, more than a decade after those experiments, the first commercially available cutaneous picosecond laser unit became available (Cynosure, Westford, Massachusetts), and several pilot studies have demonstrated its utility in tattoo removal. An experimental picosecond infrared laser has also recently demonstrated a nonthermal tissue ablative capability in soft tissue, bone, and dentin. In this article, we review the published data pertaining to dermatology on picosecond lasers from their initial reports to the present as well as discuss forthcoming technology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".