Bibliographic record
Abstract
PURPOSE OF REVIEW: Although recorded evidence of phenotyping bleeding disorders extends back two millennia, standardization of phenotyping has only begun in the past half century. This was spurred by the need for greater precision in diagnosing disorders in order to select proper laboratory tests and treatment, and the realization that the bleeding history provides prognostic information about the future risk of bleeding with surgery or invasive procedures. RECENT FINDINGS: New bleeding assessment tools (BATs) have been developed, firstly, to evaluate the relative bleeding risks associated with new anticoagulants and antiplatelet agents, secondly, to assess the efficacy of new thrombopoiesis stimulating agents in preventing hemorrhage in patients with immune thrombocytopenia, and finally, to assess complex gene-gene and gene-environment interactions. New web-based systems allow many researchers to collaborate by sharing the same electronic phenotyping infrastructure. Major issues of validation remain, but at present the data indicate that the new BATs have relatively high negative predictive value for excluding a significant bleeding disorder, but disappointingly low positive predictive values. SUMMARY: New instruments to phenotype bleeding have been developed to address a number of different important clinical and research goals. The improved standardization and opportunities for collaborative studies hold promise for maximizing diagnostic, prognostic, and scientific information.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".