Measurement of Nocturnal Scratching in Patients with Pruritus Using a Smartwatch: Initial Clinical Studies with the Itch Tracker App
Bibliographic record
Abstract
Three clinical studies were conducted to test a newly-developed app for smartwatches, which included an algorithm to measure nocturnal scratching using acceleration data. The first study in 5 patients with atopic dermatitis demonstrated high reliability of the app for measurement of scratching compared with video monitoring (positive predictive value 90.2 ± 6.6%, sensitivity 84.6 ± 10.2%, correlation of scratching duration per h r = 0.851-0.901, p < 0.001). The second study in 20 patients with atopic dermatitis and 10 healthy volunteers showed that total scratching duration in patients was significantly longer than in healthy volunteers and correlated positively with Eczema Area and Severity Index (EASI) scores. In the third study, conducted in an open-entry manner in which 201 evaluable participants measured nocturnal scratching, those who self-reported itch or pruritic diseases had a significantly longer duration of scratching than those who did not. In conclusion, this app has a high reliability and potential clinical usefulness for measurement of nocturnal scratching.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".