Commentary on: Micro-Needling Depth Penetration, Presence of Pigment Particles, and Fluorescein-Stained Platelets: Clinical Usage for Aesthetic Concerns
Bibliographic record
Abstract
I believe that this is an important paper1 that explores important questions about the popular mechanical “micro-needling pen”—a type of device for needling that to date is not adequately addressed in the literature regarding skin needling. The authors have done a difficult and rigorous investigation to show the best treatment methods and the clinical effects. I highly recommend this paper to everyone who needles skin, and I do have some comments that I believe are pertinent. I believe that the most important fact established by the authors is that with pen type instruments, one cannot rely on the settings of the needle cartridge when selecting a needle length more than 1.0 to 1.5 mm. It is important for people to understand this and recognize also that 1.0 to 1.5 is a very effective depth to achieve good results as shown in this paper. The clinical photographs confirm that shallower needling at 0.5 to 1.0 mm (in fact minimal or no bleeding) gives less evidence of collagen/elastin regeneration and, in my opinion, show the effects of the enhancement of the cosmeceuticals used. On the other hand, with deeper needling at 1.5 mm, where one would expect to see bleeding, the results are more profound. The histologic photographs are excellent and instructive, and the clinical results of using various needle depths are very useful. I found the studies on massage after needling to be interesting. However, I did not understand exactly what massaging “in the same pattern and frequency as performed during MN” means. Do they mean that the Dermapen is used again? Surely it should replicate what we do clinically? The studies on radiolabeled platelets is very instructive in showing that they are well absorbed into the dermis and then disperse. It is my impression that the variability of results …
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".