Psoriatic and psoriatic arthritis patients with and without jet-lag: does it matter for disease severity scores? Insights and implications from a pilot, prospective study
Bibliographic record
Abstract
Background: Jet-lag may affect air-travelers crossing at least two time-zones and has several health-care implications. It occurs when the human biological rhythms are out of synch with respect to the day-night cycle at the country destination. Its effect in psoriasis is missing. We aimed to evaluate the effect of Jet-lag in psoriatic patients’ management. Methods: This is a prospective observational study that enrolled psoriatic patients that underwent a flight: patients who experienced jet-lag were compared to patients who did not experience jet-lag. Before the flight, a dermatologist recorded clinical and demographical data with particular attention to Psoriasis Area Severity Index (PASI) and Disease Activity in Psoriatic Arthritis (DAPSA). Patients performed Self-Administered Psoriasis Area Severity Index (SAPASI), the Dermatology Life Quality Index (DLQI) and the pruritus Visual Analog Scale (VAS) scores. After the flight, patients completed the SAPASI, DLQI and pruritus-VAS scores. Results: The sample recruited comprised of 70 psoriatic patients aged 42.4 ± 9.7 years (median 42.5 years). Thirty (42.9%) were males, mean BMI was 25.5 ± 2.2 kg/m2. Average disease duration was 15.2 ± 7.1 years, and 20 (28.6%) subjects had developed PsA. Average hours of flight were 5.4 ± 3.5 (median 3.5 h), with 34 (48.6%) subjects reporting jet-lag. At the multivariate regression analysis, the change in the SAPASI score resulted correlated with jet-lag (regression coefficient 1.63, p = .0092), as well the change in the DLQI score (regression coefficient = 1.73, p = .0009), but no change on the pruritus VAS scale was found. Conclusions: The present study suggests that jet-lag may influence disease severity and DLQI scores, but not itch in psoriatic patients.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".