Misdiagnosis of primary immune thrombocytopenia and frequency of bleeding: lessons from the McMaster ITP Registry
Bibliographic record
Abstract
/L from a tertiary hematology clinic in Canada were eligible. Patients completed a panel of investigations and were managed per clinical need. Two hematologists initially determined the cause of the thrombocytopenia using standard criteria and reevaluated the diagnosis over time, which was adjudicated at regular team meetings. Bleeding was graded from 0 (none) to 2 (severe) prospectively using an ITP-specific tool. Data were validated by duplicate chart review and source verification. Between 2010 and 2016, 614 patients were enrolled. Median follow-up for patients with >1 visit was 1.7 years (interquartile range, 0.8-3.4). At registration, 295 patients were initially diagnosed with primary ITP; of those, 36 (12.2%) were reclassified as having a different diagnosis during follow-up. At registration, 319 patients were initially diagnosed with another thrombocytopenic condition; of those, 10 (3.1%) were ultimately reclassified as having primary ITP. Of 269 patients with a final diagnosis of primary ITP, 56.5% (95% confidence interval [CI], 50.4-62.5] experienced grade 2 bleeding at 1 or more anatomical site, and 2.2% (95% CI, 0.8-4.8) had intracranial hemorrhage. Nearly 1 in 7 patients with primary ITP were misdiagnosed. Grade 2 bleeding was common. Registry data can help improve the clinical and laboratory classification of patients with ITP.
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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 | 0.020 | 0.141 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".