Diagnosis and Characteristics of Presentation of Tubulointerstitial Nephritis and Uveitis Syndrome During the COVID-2019 Pandemic
Bibliographic record
Abstract
PURPOSE: To compare the diagnosis and clinical features of tubulointerstitial nephritis and uveitis syndrome (TINU) before and during the COVID-19 pandemic. METHODS: Retrospective chart review. RESULTS: Before the COVID-19 pandemic (March 2017 to March 2019), 1/561 (0.18%) new patient was diagnosed with TINU. During the pandemic (March 2020 to March 2022), 15/581 (2.58%) new patients were diagnosed with TINU. We found a significant increase in TINU cases during the pandemic (P=0.0005). Various posterior segment findings were observed in 2/3 (66.7%) patients before the pandemic and 13/15 (86.7%) patients during the pandemic, including disc edema, chorioretinal scars, disc leakage, and peripheral vascular leakage. CONCLUSION: This is the first study reporting an increased number of TINU during the COVID-19 pandemic. With most of the American population now exposed to COVID-19, a large multi-center epidemiological study would be helpful to investigate any association of COVID-19 disease or vaccination with TINU in recent years.
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 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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".