P076: Choosing Wisely: hemoglobin transfusions and the treatment of iron deficiency anemia
Bibliographic record
Abstract
Introduction: Choosing Wisely Canada has identified blood transfusions as a priority area for improving clinical appropriateness. Relevant recommendations include Dont transfuse blood if other non-transfusion therapies or observation would be just as effective. In parallel with this recommendation, the Alberta division of Towards Optimized Practice (ToP) has developed guidelines for the treatment of iron deficiency anemia (IDA) that emphasize the use of non-transfusion therapies (i.e. parenteral or oral iron, in appropriate patients). Choosing Wisely also emphasizes strategies to better engage patients in shared decision making. Methods: In order to better engage patients in shared decision making about their treatment options, both physician and patient handouts were developed using an iterative process. The development of the patient-facing documents began with a synthesis of educational materials currently available to patients with IDA. Clinical leaders from nine different specialties (Emergency Medicine, Family Medicine, Day Medicine, Hematology, and others) were continually engaged in the development of content using a consensus model. A focus group of ESCN patient advisors was assembled to review materials with an emphasis on: (1) Are the patient materials easily understood? (2) Are intended messages resonating while avoiding unintended messaging? (3) What information do patients require that has not been included? Following the focus group, revisions were made to patient materials and a subsequent online survey confirmed that the final version addressed any issues they had raised. Results: A four-page patient handout/infographic was developed utilizing best practices in information design, and in physician and patient engagement. Content includes the causes and symptoms of IDA, progressive treatment options from dietary changes to transfusion, and the four Choosing Wisely questions to discuss with your doctor. Conclusion: Patient education materials can be developed according to best practices in information design and stakeholder engagement. Patient focus groups demonstrate that such materials are easier to understand, and better equip patients to engage in shared decision making.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".