The association of point-of-care coagulation testing with bleeding symptoms in patients with Ehlers–Danlos syndrome: an exploratory cross-sectional study
Bibliographic record
Abstract
PURPOSE: Ehlers-Danlos syndromes (EDS) are a group of connective tissue disorders associated with an increased risk of bleeding. We examined the association between point-of-care (POC) testing parameters with bleeding history in a cohort cared for at the GoodHope EDS Clinic at Toronto General Hospital. METHODS: We conducted an exploratory cross-sectional study at the Toronto General Hospital GoodHope EDS Clinic, recruiting adult patients with a diagnosis of EDS from 27 April 2022 to 7 October 2024. Patients provided samples for rotational thromboelastometry (ROTEM) and functional platelet testing (PlateletWorks), and were administered the International Society on Thrombosis and Haemostasis Bleeding Assessment Tool (ISTH-BAT) to calculate the Bleeding Severity Score (BSS). The association between the BSS and POC parameters was examined using descriptive statistics and Spearman correlation analysis. RESULTS: We recruited 17 patients (hypermobile subtype, n = 10; classical subtype, n = 7). Clinically significant bleeding (BSS ≥ 4 in males; ≥ 6 in females) was reported in the majority (classical group, 86%; hypermobile group, 90%). Nine (53%) patients had major surgery or trauma at a median age of 23 (range, 8-39) yr. There were no significant abnormalities detected using ROTEM. Lower functional platelet percentages were observed in the hypermobile subgroup, with no association with BSS. CONCLUSIONS: Point-of-care functional platelet testing using aggregation in response to an exogenous collagen agonist may detect inherent platelet dysfunction in patients with hypermobile subtype. We did not detect any ROTEM parameter abnormalities that were associated with elevated BSS scores. This supports a multifactorial etiology of bleeding in this patient population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".