Diagnostic Dilemma of ANA-negative Pediatric Systemic Lupus Erythematosus in a South Asian Female
Bibliographic record
Abstract
Background: Systemic lupus erythematosus (SLE) is an autoimmune disorder affecting multiple organs with different degrees of severity. SLE is typically diagnosed based on the presence of antinuclear antibodies (ANA) in the serum. However, seronegative SLE is rare and is diagnosed by clinicians when the patient's ANA is negative but fulfills other diagnostic criteria. Case report: We report a case of a 15-year-old South Asian female with SLE who had negative antinuclear antibodies yet displayed the typical clinical presentations of photosensitive maculopapular rash, joint pain, alopecia, anemia, and thrombocytopenia. Clinical evaluations in conjunction with lab results were used to establish a diagnosis of ANA-negative SLE. Conclusion: ANA positivity is an entry criterion for SLE; rarely, cases of ANA-negative SLE may present. A typical clinical presentation may help determine the diagnosis in such a scenario. However, still, the physician should rule out immunodeficiency and other systemic illnesses before reaching a diagnosis of ANA-negative pediatric SLE.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".