CAUSES OF HOSPITALIZATION IN SYSTEMIC LUPUS ERYTHEMATOSUS PATIENTS AT A TERTIARY CARE CENTER IN NEPAL
Bibliographic record
Abstract
PV232a / #824 Poster Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes Background/Purpose Systemic Lupus Erythematosus (SLE) is a chronic autoimmune disease with multisystem involvement, primarily affecting the mucocutaneous, musculoskeletal, and renal systems. Patients with SLE often require hospitalization due to disease flares, infections, or complications related to organ involvement. While extensive data exist on SLE-related hospitalizations in Western countries, similar studies from developing regions are limited. This study aims to identify the most common reasons for hospital admissions and clinical outcomes of SLE patients in a tertiary care center in Nepal. Methods This descriptive cross-sectional study was conducted in the Department of Internal Medicine at B.P. Koirala Institute of Health Sciences (BPKIHS), Dharan, Nepal, from November 7, 2023, to November 6, 2024. Ethical approval was obtained from the Institutional Review Committee (Reference number: 223/080/081). Patients diagnosed with SLE for at least 3 months, based on the Systemic Lupus International Collaborating Clinics (SLICC) criteria, were included. Results A total of 64 patients were analyzed, with a female predominance (96.9%) and a mean age of 32.37 ± 11.10 years. Hypertension (15.6%) and hypothyroidism (12.5%) were the most common comorbidities. The mean duration of SLE diagnosis was 61.78 ± 57.63 months. The leading causes of hospitalization were renal flare (39.1%) and infection (20.3%), followed by volume overload (9.4%), evaluation (7.8%), and hematological and neurological flares (4.7% each). The average hospital stay was 5.34 ± 3.03 days Conclusions Renal flares and infections were the most frequent causes of hospitalization among SLE patients. These findings align with data from developed countries, emphasizing the need for early recognition and management of these complications to reduce hospital admissions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".