151 Coproduction of statements to communicate uncertainty from ambiguity and complexity with caregivers of children with neuromuscular scoliosis
Bibliographic record
Abstract
Introduction Ambiguity and complexity are common sources of uncertainty in medical decisions for children with medical complexity (CMC).1 Little is known about how to communicate ambiguity and complexity to caregivers. This study coproduced with caregivers of CMC a clinical vignette with statements of uncertainty for use in a clinical trial. Methods We conducted cognitive interviews with English- and Spanish-speaking adult caregivers of CMC from two children’s hospitals. Caregivers reviewed a vignette of a provider discussing with a caregiver whether to pursue neuromuscular scoliosis surgery for their child. The vignette was based on prior caregiver interviews and included embedded statements expressing ambiguity and complexity. Semi-structured interviews assessed comprehension, interpretation of uncertainty, and realism. After each session, two study team members analyzed participant in-text edits, interview responses, and field notes; synthesized key interpretations, and reached agreement on modifications to the vignette. The vignette was iteratively revised and interviews continued until no new substantive feedback arose. Results From interviews with 22 participants (6 Spanish, 11 with scoliosis), we refined the vignette to better portray ambiguity and complexity. We found that for ambiguity statements, caregivers wanted examples directly related to the patient, not abstract examples (e.g., inserting the patient’s name into each statement). For complexity statements, rather than a list of all conditions contributing to complexity, caregivers needed distinct statements about how each condition affected a specific risk or benefit. Unexpectedly and unsolicited, a statement about uncertainty being normal and expected during decision making elicited polarized feedback. Participants either felt strongly that the statement demonstrated provider understanding that CMC face immense uncertainty or showed provider incompetence. Discussion Co-producing uncertainty statements with caregivers resulted in critical changes to improve acceptability and interpretation. Conclusion Next, we will test these statements in a clinical trial to inform presentation of ambiguity and complexity in a forthcoming decision aid. Reference Han PJK, Klein WMP, Arora NK. Med Decis Making. 2011;31(6):828–838.
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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 | 0.019 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".