Misdiagnosis of primary immune thrombocytopenia and frequency of bleeding: lessons from the McMaster ITP Registry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it