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.
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.
How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 teacher head, 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".