Antera 3D camera: A novel method for evaluating the therapeutic efficacy of fractional CO<sub>2</sub> laser for surgical incision scars
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: laser for incision scars. METHODS: A total of 72 patients after incision healing for at least 2 years were included in the series, with scars on neck, thyroid, chest, and limb from September 2013 to September 2016. The image of scar was taken by Antrea 3D camera before the treatment, VSS, and UN4P were also applied for scar evaluation. A total of four sessions at 4-6 intervals were conducted to each patient. After 3 months of last session, a final assessment was carried out by Antera 3D and VSS, UN4P independently. RESULTS: The Antera scores for color after 4 sessions were 8.78 ± 2.11, which were significantly lower than the prior treatment (9.62 ± 1.90, t = 2.51, P < 0.05). The Antera scores for texture after four sessions were 22.80 ± 5.23, which was significantly lower than the prior treatment (30.33 ± 5.41, t = 8.48, P < 0.05). The Antera scores for melanin levels after four sessions were 0.52 ± 0.05, which was significantly lower than the prior treatment (0.54 ± 0.05, t = 2.4, P < 0.05). The Antera scores for hemoglobin levels after four sessions were 1.88 ± 0.50, which was significantly lower than the prior treatment (2.11 ± 0.45, t = 2.90, P < 0.05). The Vancouver Scar scores after four sessions were 7.1 ± 2.0, which showed no statistically significant differences with the prior treatment (7.5 ± 2.4, t = 1.09, P = 0.25 > 0.05). The University of North Carolina "4P" Scar scores after four sessions were 6.6 ± 1.5, which also showed no statistically significant differences with the prior treatment (7.0 ± 1.9, t = 1.40, P = 0.15 > 0.05). CONCLUSIONS: For scar therapeutic evaluation, Antera 3D camera is objective and accurate, and is worthy of wide promotion.
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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 | 0.001 |
| 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 it