“Normal” TEWL‐how can it be defined? A systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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