Abstract O.75: Factors Associated With Illness Impact After Diagnosis Of Kawasaki Disease And Coronary Artery Complications
Bibliographic record
Abstract
Background: Families with Kawasaki disease (KD) experience profound anxiety, part of which is related to uncertainties about the future. We sought to understand the personal and emotional impact of uncertainty for both parents and children with coronary artery complications. Method: During 2013-14, 31 participants were recruited. Data collection included chart reviews of demographic factors, relevant medical history, investigations, and treatments. Parents and children completed questionnaires (uncertainty, intrusiveness, self-efficacy) and were interviewed. The qualitative data were analyzed for common themes and exemplars in order to complement the quantitative questionnaire data. Findings: Descriptive data were compared with questionnaire scores to identify factors associated with high, negative impact using univariable linear regression models. High intrusiveness scores among parents were associated with having a child who had previous cardiac catheterization (p =.05), received anticoagulant medications (p = .04), lower education (p = .02 [mother], p = .04 [father]) and income (p = .05), and for those in whom the KD diagnosis was initially missed (p <.001). Higher uncertainty scores among children were associated with absence of chest pain (p = .04) and lower number of echocardiograms (p = .01). Parents’ uncertainty was associated with missed diagnosis (p = .002), higher education (p = .03 [mother]), and higher income levels (p = .01). Self efficacy was assessed among children >10yrs. While 3 subscales (academic, social, emotional) were analyzed, the overall self-efficacy scores increased with the presence of chest pain (p = .003) and increased aneurysms z-score (p =.03). Qualitative analysis revealed 3 main themes: 1) staying normal while hyper-vigilant; 2) optimism amid relentless worry; and 3) healthy present for a hopeful future. The themes involved contrasting sentiments, each of which was held by the child or the parent but with varied levels of expression. Summary: Negative illness impact is associated with a more intense medical experience. Both children and parents have concerns about future outcomes and management. Coping with uncertainty involves achieving a balance between anxieties and a present and forward focus.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".