Multispectral near‐infrared spectroscopy study evaluating the effect of razor design on shaving‐induced erythema
Bibliographic record
Abstract
BACKGROUND: While shaving-induced erythema is a common inflammatory skin issue, there is a lack of quantitative information on how well a shaving product performs in this regard. In this study, multispectral near-infrared spectroscopy (NIRS) imaging was used to quantitatively and qualitatively measure the extent of shaving-induced erythema. The research compares a safety razor and a cartridge razor to evaluate their impact on skin irritation. MATERIALS AND METHODS: Fifty-nine healthy male volunteers without pre-existing skin conditions were enrolled. Basic demographics were recorded, and participants' faces or necks were imaged before shaving. Shaving was conducted on the right side of the face/neck with the safety razor and on the left side of the face/neck using the 3-blade cartridge razor. Images were captured immediately after shaving, at 5 and 10 min post-shaving. RESULTS: Tissue oxygen saturation (StO2) measurements demonstrated that the safety razor induced significantly less erythema than the cartridge razor. Immediately after shaving, 40.3% of skin shaved with the safety razor had erythema compared to 57.6% for the cartridge razor. At 5 min post-shaving, 36.5% of skin shaved with the safety razor had erythema, compared to 53.8% of cartridge razor. CONCLUSIONS: Multispectral NIRS revealed significant differences in shaving-induced erythema between safety and cartridge razors. Safety razors demonstrated a lower incidence of erythema, suggesting a potential advantage for individuals prone to skin irritation. This study contributes valuable insights into skin irritation and highlights the potential of multispectral NIRS in dermatology research.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".