“Normal” TEWL‐how can it be defined? A systematic review
Bibliographic record
Abstract
Trans-epidermal water loss (TEWL), the total non-eccrine sweat water evaporating from a given area of epidermis over time, is a measurement of skin barrier integrity. Skin diseases (e.g., psoriasis and atopic dermatitis) often result in transient increases in TEWL, so, knowledge of "normal" TEWL values may be used to predict disease progression in dermatological settings. Variables such as age, race and anatomic location have been suggested to affect TEWL, but current regulatory agencies have failed to control for additional variables of interest. Thus, this review summarizes variables that may cause TEWL variation. A comprehensive literature search was performed using Embase, PubMed and Web of Science to find human studies that provided data on variables affecting TEWL. 31 studies, analysing 22 affecting TEWL, were identified. Variables causing increased TEWL were mask-use (n = 1), dry eye disease (n = 1), chronic venous disease (n = 1), coronary artery disease (n = 1), age (infants vs adults) (n = 4), nourishment in infants (n = 1), stress within individuals (n = 2), Body Mass Index (n = 2), bathing versus showering (n = 2) and scratching/friction (n = 1). Variables with decreases in TEWL were genetic variability with SNPs on chromosome 9q34.3 (n = 1) and cancer-cachexia (n = 1). We summarized 12 variables that impact TEWL and are not typically controlled for in experimental settings. Therefore, defining normal TEWL may currently be problematic. Thus, regulatory agencies should provide stricter guidelines on proper measurement of TEWL to minimize human introduced TEWL variation, and we should continue to examine factors impacting individual skin integrity.
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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 | 0.014 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".