EMERGENCY MANAGEMENT OF CRITICAL BLEEDS IN PATIENTS WITH IMMUNE THROMBOCYTOPENIA: DEVELOPING A NOVEL METHODOLOGY FOR RARE DISEASES GUIDELINES
Bibliographic record
Abstract
Developing clinical practice guidelines (CPGs) for rare diseases is methodologically challenging. As each disease has so few patients, published literature includes low-quality studies or studies that do not directly address the questions of interest. As a result, CPG panelists have limited evidence on which to base their recommendations. Historically, when no evidence was available, CPGs have relied on physician opinion. This does not align with the mandate of CPGs which transparently identifies, appraises, and relies on evidence. The challenges of developing CPGs for rare diseases are exemplified by immune thrombocytopenia (ITP), a rare autoimmune disease that affects approximately 1 in 8,000 people. It predominantly affects females and young adults, and is characterized by low blood platelets that increase the risk of bleeding. Bleeding emergencies in ITP patients are critical, life-threatening events that can cause life-long morbidity and associated health care costs. Treatment of ITP bleeding emergencies requires a rapid, coordinated approach that involves emergency department staff, hematologists, pharmacy, and the laboratory. However, there is no evidence-based CPG for the management of ITP bleeding emergencies. The objectives of my PhD thesis are (1) exploring the heterogeneity of ITP diagnosis using antiplatelet autoantibodies; (2) developing a standardized definition of ITP bleeding emergencies; (3) outlining the synthesis of existing evidence on the treatment of ITP bleeding emergencies through a systematic review; and (4) developing a novel methodology to address the lack of evidence in rare disease CPGs and applying it to develop a CPG for the management of ITP bleeding emergencies.
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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 | 0.026 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".