Closing the Serological Gap in the Antiphospholipid Syndrome: The Value of “Non-criteria” Antiphospholipid Antibodies
Bibliographic record
Abstract
OBJECTIVE: Most clinicians use the 2006 Sydney classification criteria to evaluate patients suspected of having antiphospholipid syndrome (APS). Although sensitive and specific for APS, many patients fulfilling clinical criteria for the syndrome are persistently negative for the specific serological tests ("laboratory criteria"). These "seronegative APS" (SN-APS) patients can go undiagnosed and untreated until they experience serious clinical events. This study's objective was to describe antibody profiles of SN-APS patients using non-criteria markers, assess the clinical utility of these markers separately and in combination, and suggest incorporation into guidelines for patients suspected of APS. METHODS: We categorized 175 consecutive patients suspected of APS into 2 subgroups: 107 fulfilling Sydney APS classification for seropositive APS (SP-APS) and 68 with clinical manifestations suggestive of APS but having negative serology, on 2 occasions, for criteria markers (SN-APS). On study inclusion, samples were retested for criteria and 11 non-criteria markers, including antiphosphatidylserine/prothrombin antibodies. RESULTS: Using 4 of 11 non-criteria tests, a cumulative 30.9% of SN-APS patients were detected. Combining results of all 11 non-criteria tests, 25 SN-APS (36.8%) and 89 SP-APS (83.2%) were positive for 1 or more non-criteria antibodies. CONCLUSION: Failure to diagnose APS can result in severe clinical consequences. Patients displaying clinical features of APS, but negative for conventional criteria markers, should undergo additional testing for non-criteria biomarkers. In our cohort, around one-third of SN-APS patients showed reactivity to 1 or more non-criteria markers. An update to the current classification criteria incorporating new serological markers should be considered to identify and stratify patients with APS for more effective treatment and management.
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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 | 0.003 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".