Using Decision Analysis Techniques to Deal With ???Unanswerable??? Questions in Idiopathic Thrombocytopenic Purpura
Bibliographic record
Abstract
Idiopathic thrombocytopenic purpura (ITP) is a common disorder with rare adverse outcomes. This makes it a particularly difficult area in which to undertake conventional studies. An alternative method for solving clinical questions is decision analysis, which is in essence a computer-assisted synthesis of the literature. Using the example of a newly diagnosed ITP patient, the author attempts to answer the question of whether a bone marrow aspirate (BMA) is required prior to starting steroids. Using decision analysis methodology, the author determines that BMA is not essential prior to starting steroids. More importantly, three variables critical to the decision-making process are determined: the risk of death from the BMA procedure, the altered chance of survival for a patient with acute lymphoblastic leukemia (ALL) inappropriately given steroids, and how sensitive the complete blood count is at determining the risk of ALL. This scenario demonstrates the value of decision analysis and lays the groundwork for future endeavors.
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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 | 0.047 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".