PB1352 A Survey on Anticoagulation in Patient with ITP
Bibliographic record
Abstract
Background: Vaccine-induced immune thrombotic thrombocytopenia (VITT) has been described following adenovirus vector-based COVID-19 vaccines.This condition is associated with important morbidity and mortality following thrombosis related complications.Diagnosis is confirmed based on results of platelet factor 4 ELISA detecting anti-PF4 antibodies and of platelet-activation assay.Initial treatment strategy has been established but long-term management and follow up remain unclear.Most plateletactivation tests become negative after 12 weeks.Aims: Persistence of platelet-activating anti-PF4 antibodies has been observed in cases described as long VITT.This condition is challenging and needs to be defined.Methods: We describe a case of VITT which can now be characterized as long VITT.Results: The patient initially had a lower limb ischemia, pulmonary embolism and cerebral vein thrombosis.He was treated with prednisone, IVIG, argatroban and had a lower limb revascularization surgery.Rivaroxaban was then initiated for the acute treatment and continued for the secondary prevention of recurrent events.The patient still demonstrates positive platelet-activation tests and thrombocytopenia after more than 18 months of follow-up.No recurrent thrombosis or bleeding event have occurred and no IVIG were needed beyond initial hospitalization.He is not known for any relevant past medical history other than alcohol consumption and slight thrombocytopenia (130 × 10^9/L since 2015).It is unclear if the ongoing and more important thrombocytopenia could be explained by the persistent platelet-activating anti-PF4 antibodies or the patient's habits.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".