Effect of multifunctional laser photoelectricity platform combined with hydroxychloroquine treatment sensitive facial skin
Bibliographic record
Abstract
To observe the clinical efficacy of a multifunctional laser photoelectric platform combined with hydroxychloroquine in the treatment of l sensitive facia skin. A total of 226 patients with sensitive facial skin treated from March 2019 to November 2021 were randomly divided into two groups. Both groups were given an external moisturizer (shumin moisturizer) once in the morning and once in the evening as basic skin care treatment for the disease. The control group received hydroxychloroquine sulfate tablets (0.2 g), twice a day; The observation group was treated with multifunctional laser photoelectric platform combined with hydroxychloroquine orally, and the clinical effects of the two groups were compared. The effective rates of the observation group were 48.67%, 73.45%, and 93.80% at the first, second and fourth weekend of treatment, respectively, which were significantly higher than those of the control group (15.93%, 30.97%, and 38.93%, respectively), with statistical significance (p < 0.05). On the basis of skin care with an external moisturizer, using a multifunctional laser photoelectric platform combined with hydroxychloroquine can significantly improve the clinical efficacy of facial sensitive skin, superior to hydroxychloroquine alone, with high safety and worthy of clinical application.
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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) | 0.000 | 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) | 0.001 | 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".