Mortality in Psoriatic Arthritis patients, changes over time and the impact of COVID-19: Results from a multicenter Psoriatic Arthritis Registry (PsART-ID)
Bibliographic record
Abstract
Abstract BackgroundThis study aimed to assess the mortality of PsA before and during the COVID-19 pandemic.MethodsFrom the prospective, multicenter PsART-ID (Psoriatic Arthritis Registry-International Database), patients from Turkey were analyzed by linking the registry to the Turkish Cause of Death Registry. The outcome of interest was death from any cause, pre-pandemic (since the onset of registry – March 2014- March 2020), and during the pandemic (March 2020-May 2021). The crude mortality rate and standardized mortality ratio (SMR) were determined. ResultsThere were 1216 PsA patients with a follow-up of 7500 patient-years. Overall, 46 deaths (26 males) were observed. In the pre-pandemic period, SMR for PsA vs the general population was 0.95 (0.61-1.49), being higher in males [1.56 (0.92-2.63)] than females [0.62 (0.33-1.17)]. The crude mortality rate in PsA doubled during the pandemic (pre-pandemic crude mortality rate: 5.07 vs 10.76 during the pandemic) with a higher increase in females (2.9 vs 8.72) than males (9.07 vs 14.73).Conclusion The mortality in PsA was found similar to the general population in the pre-pandemic era. The mortality rates in PsA doubled during the pandemic. Whether PsA patients have more risk of mortality than the general population due to COVID-19, needs further studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".