<p>Nonadherence to Treatment and Patient-Reported Outcomes of Psoriasis During the COVID-19 Epidemic: A Web-Based Survey</p>
Bibliographic record
Abstract
PURPOSE: The COVID-19 epidemic has caused difficulties in continuous treatment for patients with chronic diseases and resulted in nonadherence to treatment and adverse health outcomes. This study aimed to investigate the associations of nonadherence to treatment with patient-reported outcomes of psoriasis during the COVID-2019 epidemic. METHODS: A cross-sectional study among Chinese patients with psoriasis was conducted through a web-based questionnaire survey during 25 Feb 2020 and 6 Mar 2020. Demographic and clinical data, nonadherence to treatment, and patient-reported outcomes were collected. The outcomes included deterioration of the disease condition, perceived stress, and symptoms of anxiety and depression. Logistic regression was used to investigate the associations. RESULTS: A total of 926 questionnaires were collected. A total of 634 (68.5%) reported nonadherence to treatment, and worse adherence was found among patients receiving systemic treatment (adjusted odds ratio [AOR]: 2.67; 95% CI: 1.40-5.10) and topical treatment (AOR: 4.51; 95% CI: 2.66-7.65) compared to biological treatment. Nonadherence to treatment (less than two weeks and more than two weeks) was significantly associated with deterioration of psoriasis (aOR: 2.83 to 5.25), perceived stress (AOR: 1.86 to 1.57), and symptoms of anxiety (AOR: 1.42 to 1.57) and depression (AORs: 1.78). Subgroup analysis by treatment showed consistent results in general. CONCLUSION: Nonadherence to treatment was associated with the aggravation of psoriasis conditions, perceived stress, and symptoms of anxiety and depression.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".