Should aPS/PT Be Incorporated into the Routine Serological Tests in the Diagnosis of Antiphospholipid Syndrome?
Bibliographic record
Abstract
We read with great interest the article by Zohoury, et al 1 on how to close the serological gap in the diagnosis of antiphospholipid syndrome (APS) by using non-criteria antiphospholipid antibodies (aPL). In their well-designed study, the authors found that using 4 of 11 non-criteria tests [antiphosphatidylserine/prothrombin complex (aPS/PT), antiphosphatidylserine (aPS), antiphosphatidylethanolamine antibodies, and anticardiolipin (aCL)/vimentin antibodies], an accumulative 30.9% of seronegative APS (SN-APS) patients were detected, and there was a further 5.9% increase when using the other 7 non-criteria tests. On the basis of their findings, the authors concluded that patients displaying clinical features of APS but negative for conventional criteria markers should undergo additional testing for non-criteria biomarkers. Among those non-criteria biomarkers, aPS/PT has exhibited the most promising potential owing to the availability of the well-characterized and standardized commercial ELISA kits2. In this letter, we hope to contribute to this discussion by calling attention to an additional report that we recently published on the clinical relevance of aPS/PT in Chinese patients with APS3. In our study, sera from 441 subjects were analyzed, including … Address correspondence to Dr. Y. Li, Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, No. 1 Shuai Fu Yuan, Eastern District, Beijing 100730, China. E-mail: LiYZ{at}pumch.cn or yongzhelipumch{at}126.com
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".