150 Uncertainty communication in caregivers of children with neuromuscular scoliosis: a qualitative study
Bibliographic record
Abstract
Introduction Surgical treatment is the only definitive treatment to halt the progression of neuromuscular scoliosis (NMS) but is associated with complication rates of 17–40%. Caregivers experience considerable uncertainty during decision-making. This study aims to identify topics for which uncertainty is a concern to caregivers and how caregivers interpret that uncertainty. Methods From two quaternary children’s hospitals, we recruited English- and Spanish-speaking adult caregivers of children aged 8–21 years with NMS who had previously decided whether to treat their child’s NMS with surgery. Caregivers completed an audio-recorded 45–60 minute semi-structured interview about uncertainty related to NMS treatment decisions. Two independent coders used thematic analysis of interview transcripts to inductively generate themes and serial coding reviews to generate agreement. Team meetings synthesized final themes. Subsequently, themes were categorized into an existing taxonomy of sources of uncertainty: probability, ambiguity, and complexity.1 Results From n=45 interviews, we identified six topical areas of uncertainty: right time for surgery, rate of NMS progression, benefits and risks of surgery, risks of non-surgical management, and effects of underlying comorbidities (see table 1 for quotes). The effects of comorbidities was the most commonly mentioned uncertainty, and the only uncertainty for which caregivers served as the information source rather than providers. Except for treatment risks and benefits, caregivers interpreted most uncertainty as arising from ambiguity or complexity. Discussion Clinical encounters should better address how underlying comorbidities affect the risks and benefits of NMS treatment options. Conclusion Successful strategies to communicate uncertainties from ambiguity and complexity are needed to address this issue. Reference Han PJK, Klein WMP, Arora NK. Med Decis Making. 2011;311(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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".