Bleeding risks for uncharacterized platelet function disorders
Bibliographic record
Abstract
Background The bleeding risks for nonsyndromic platelet function disorders (PFDs) that impair aggregation responses and/or cause dense granule deficiency (DGD) are uncertain. Objectives Our goal was to quantify bleeding risks for a cohort of consecutive cases with uncharacterized PFD. Methods Sequential cases with uncharacterized PFDs that had reduced maximal aggregation (MA) with multiple agonists and/or nonsyndromic DGD were invited to participate along with additional family members to reduce bias. Index cases were further evaluated by exome sequencing, with analysis of RUNX1-dependent genes for cases with RUNX1 sequence variants. Bleeding assessment tools were used to estimate bleeding scores, with bleeding risks estimated as odds ratios (ORs) relative to general population controls. Relationships between symptoms and laboratory findings were also explored. Results Participants with uncharacterized PFD (n = 37; 23 index cases) had impaired aggregation function (70%), nonsyndromic DGD (19%) or both (11%), unlike unaffected relatives. Probable pathogenic RUNX1 variants were found in 2 (9%) index cases/families, whereas others had PFD of unknown cause. Participants with PFD had increased bleeding scores compared to unaffected family members and general population controls, and increased risks for mucocutaneous (OR, 4-207) and challenge-related bleeding (OR, 12-43), and for receiving transfusions for bleeding (OR, 100). Reduced MA with collagen was associated with wound healing problems and bruising, and more severe DGD was associated with surgical bleeding (P < .04). Conclusions PFDs that impair MA and/or cause nonsyndromic DGD have significantly increased bleeding risks, and some symptoms are more common in those with more severe DGD or impaired collagen aggregation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".