Seasonality and global public interest in psoriasis: an infodemiology study
Bibliographic record
Abstract
Abstract Objective Although patients with psoriasis frequently report seasonal changes in their symptoms, the seasonality of psoriasis has rarely been explored. This study aims to investigate the seasonal pattern of and global public interest in psoriasis using Google search data. Methods Internet search data were collected from Google Trends. Data on the relative search volume (RSV) from January 2004 to December 2018 were retrieved using the term psoriasis. Cosinor analyses were conducted to examine the seasonality of psoriasis using data from two southern hemisphere countries (Australia and New Zealand) and four northern hemisphere countries (USA, Canada, UK and Ireland). Results Overall, searches for psoriasis steadily decreased between 2004 and 2010, and then rose from 2011 to 2018. On cosinor analyses, RSV of ‘psoriasis’ displayed a significant seasonal variation worldwide (p<0.025). Further analyses confirmed the seasonality of psoriasis-related RSV in Australia, New Zealand, USA, Canada, UK and Ireland (p<0.025 for all), with peaks in the late winter/early spring months and troughs in the late summer/early autumn months. The top 11 rising topics were calcipotriol/betamethasone dipropionate, ustekinumab, apremilast, shampoo, eczema, guttate psoriasis, seborrhoeic dermatitis, dermatitis, psoriatic arthritis, atopic dermatitis and arthritis. Conclusion There was a significant seasonal pattern for psoriasis, with peaks in the late winter/early spring and troughs in the late summer/early autumn. Further studies are warranted to confirm the seasonal pattern of psoriasis using clinical data and to explore the underlying mechanisms.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".