Impact of the COVID-19 Pandemic on the Appropriateness of Diagnostic Pathways of Autoimmune Rheumatic Diseases
Bibliographic record
Abstract
OBJECTIVE: Early diagnosis of autoimmune rheumatic diseases (ARDs) is key to achieving effective treatment and improving prognosis. The coronavirus disease 2019 (COVID-19) pandemic has led to major changes in clinical practice on a global scale. We aimed to evaluate the impact of the COVID-19 pandemic on rheumatological clinical practice and autoimmunity testing demands. METHODS: Data regarding the first rheumatological visits and new diagnoses, together with the autoimmunity laboratory testing volumes related to the COVID-19 pandemic phase (January-December 2020), were collected from medical records and the laboratory information system of a regional reference hospital (Basilicata, Italy) and compared with those obtained during the corresponding period in 2019. RESULTS: < 0.05) was also observed. However, an equivalent or higher percentage of positive autoimmunity results from outpatient services was recorded during 2020 when compared to the prepandemic state. Of note, COVID-19-associated decline in new diagnoses affected mainly less severe diseases. In contrast, ARDs with systemic involvement were diagnosed at the same levels as in the prepandemic period. CONCLUSION: The COVID-19 pandemic has affected access to health services. However, our study highlighted that during the outbreak, greater appropriateness of the requests for laboratory tests and visits emerged, as shown by a greater percentage of positive test results and new diagnoses of more severe ARDs compared to the prepandemic period.
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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 | 0.008 |
| 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) | 0.003 | 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".