High Titers of Autoantibodies in Patients with Sickle-Cell Disease
Bibliographic record
Abstract
OBJECTIVE: Frequency and titers of autoantibodies in patients with sickle-cell disease (SCD) have been reported as relatively high. In a prospective study of 88 patients, we examined this "hyper-autoreactivity" and its clinical consequences. METHODS: For 1 year, patients with SCD were screened for the presence in their serum of antinuclear, anti-double-stranded DNA, antiextractible nuclear antigens, anticardiolipin antibodies, and rheumatoid factors. A population of 85 sex-matched individuals of similar ethnic origin served as controls. RESULTS: Whereas prevalence of autoantibodies did not differ between the 2 groups, the type and rate of antinuclear antibodies were different. Autoantibodies from the SCD patients showed various immunofluorescence patterns, whereas only speckled patterns at low titers were present in controls. No antibody specificity was found in either group. SCD patients and controls displayed similar rates of anticardiolipin antibodies, but the SCD patients tended to be more frequently positive for rheumatoid factors. Six-year followup of the SCD patients did not provide any clinical evidence for onset of an autoimmune disease, except for 1 patient who developed rheumatoid arthritis, with increasing antinuclear antibodies followed by emergence of specific markers 5 years later. CONCLUSION: Patients with SCD displayed high titers of autoantibodies. This observation may be due only to immune activation and/or dysfunction in SCD, as neither pathogenic specificity of autoantibodies nor autoimmune clinical signs appeared in the majority of cases in our study.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".