Performance characteristics of platelet autoantibody testing for the diagnosis of immune thrombocytopenia using strict clinical criteria
Bibliographic record
Abstract
Misclassification of immune thrombocytopenia (ITP) is common, which might undermine the value of platelet autoantibody testing. We determined the sensitivity and specificity of platelet autoantibody testing using the direct antigen capture assay for anti-glycoprotein (GP) IIb/IIIa or anti-GPIbIX in patients with 'definite ITP', defined as those with a documented treatment response. Sensitivity of platelet autoantiboody testing increased from 48·3% [95% confidence interval (CI) 43·5-53·2] for all ITP patients to 64·7% (95% CI 54·6-73·9) for definite ITP patients. Specificity was unchanged [75·3% (95% CI 67·5-82·1)]. High optical density values (>0·8) improved the specificity of platelet autoantibody testing but lowered sensitivity. In patients with a high pretest probability, platelet autoantibodies can aid in the diagnosis of ITP and may be most prevalent in certain patient subsets.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".