Bibliographic record
Abstract
To the Editor: Dr. Weinstein expresses disappointment that the new European League Against Rheumatism (EULAR)/American College of Rheumatology (ACR) 2019 classification criteria for SLE do not include CB-CAPs. We find the concept of CB-CAPs interesting in that low complement proteins C3 and C4 can be masked, as they are part of the acute-phase response. We also appreciate that the cited work had prominent involvement of colleagues who also played an active role in the EULAR/ACR SLE classification project 1, 2. However, we do not share Dr. Weinstein's disappointment with the EULAR/ACR 2019 classification criteria and believe that it is due, in part, to misunderstanding of the criteria. Development of new classification criteria takes several years, is unidirectional, and is based on robust evidence. The expert Delphi exercise, which supported the primary choice of candidate criteria, was completed and presented in abstract form in 2017 and published in 2018 3. Therefore, 2 of the 3 CB-CAP studies cited by Dr. Weinstein 1, 2 could not have been taken into consideration. Complement fragments (elevated EC4d and BC4d levels) were considered 3 as were many other more novel laboratory parameters, but, as with these other assays, there was insufficient consensus in the expert Delphi exercise. Many laboratory parameters (including CB-CAPs and type I interferon signature) were not selected due to insufficient evidence or unavailability worldwide. This problem is not resolved for CB-CAPs, as of today. Inclusion, in classification criteria approved by EULAR and ACR, of a test that is not broadly available in clinical practice would limit access to scientific studies. This precludes inclusion of potentially interesting tests that have not been proven robust in independent patient cohorts and hence are not fully established for clinical use in different regions of the world. There is another distinction between diagnosis and classification that appears relevant regarding CB-CAPs. CB-CAP testing was apparently optimized for sensitivity, which at 61% is much higher than the sensitivity of low C4 or C3 levels (23%) 1. This is important in a diagnostic setting, where it is often relevant to exclude an SLE diagnosis. Classification, on the other hand, aims to positively define the disease with high specificity. Therefore, low C3 and C4 complement levels, with a higher specificity (98%) compared to CB-CAPs (86%) 1, are advantageous for classification. Whether the CB-CAP assay becomes available for routine clinical use worldwide and whether its specificity for SLE will be proven to be high remain to be seen, and we would welcome validated demonstrations of improved laboratory test characteristics.
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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 | 0.004 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.019 | 0.025 |
| Insufficient payload (model declined to judge) | 0.036 | 0.025 |
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".