The utility of shared decision making in the management of hereditary angioedema
Bibliographic record
Abstract
Background: Hereditary angioedema (HAE) is a complex disorder with a wide array of treatment options. Shared decision-making (SDM) should be used to ensure that patients are choosing their best treatment option. The goal was to develop and psychometrically test a brief instrument for assessing the patient’s perspective of the SDM process during his or her clinical encounters with an HAE specialist/allergist. Method: We hypothesized that SDM could be used effectively to help patients in their choice of therapy for HAE. Ten HAE treating physicians from the United States with a total of 50 patients with HAE used SDM to help patients choose the best prophylactic therapies (oral kallikrein inhibitor, androgens, subcutaneous C1 inhibitor replacement therapy, intravenous C1 inhibitor replacement therapy, monoclonal antibody kallikrein inhibitor) for their HAE and then completed surveys to analyze the effectiveness of the implementation of SDM as a quality indicator in health services assessment. Results: The congruence of answers between the physicians and the patients was then analyzed; 90% of the patient-physician pairs agreed that the advantages and disadvantages of the treatment options were precisely explained; 92% of the patient-physician pairs agreed that the physician helped them understand all the information and that the physician asked them which treatment option they preferred; 88% of the pairs agreed that the different treatment options were thoroughly weighed and 92% of the pairs felt that they selected a treatment option together. Conclusion: In summary, SDM is being implemented by treating physicians to determine the best management options for their patients with HAE.
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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 | 0.068 | 0.174 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".