Management of patients with elevated Self-BAT scores or other bleeding symptoms
Bibliographic record
Abstract
OBJECTIVE: To provide recommendations for management of patients presenting with elevated self-administered bleeding assessment tool (Self-BAT) scores or other bleeding symptoms in a primary care setting. SOURCES OF INFORMATION: Primary research sources, clinical review articles, and interviews with research staff, hematologists, and family physicians were used to create the poster tools and reference guide resources. MAIN MESSAGE: Recommendations to manage a patient with an elevated Self-BAT score or other bleeding symptoms include reviewing the results of the Self-BAT to clarify relevant symptoms, performing initial diagnostic laboratory tests, and doing basic symptom management. Clinical judgment should be used when determining whether referral to a hematologist is necessary, but referral should be considered if the bleeding score is abnormal or if initial management options are ineffective. Some bleeding symptoms warrant evaluation by a gastroenterologist, obstetrician-gynecologist, or otolaryngologist. CONCLUSION: Primary care providers should conduct a thorough review of the Self-BAT results and associated management recommendations when working with a patient presenting with an elevated score or other bleeding manifestations. A review consists of the clarification of relevant symptoms, appropriate initial laboratory workups, and patient education. Treatment options for symptom management should be explored while recognizing the threshold for referral to a hematologist.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".