Shared Decision Making in the Management of Children With Newly Diagnosed Immune Thrombocytopenia
Bibliographic record
Abstract
This study aimed to examine the treatment decision-making process for children hospitalized with newly diagnosed immune thrombocytopenia (ITP). Using focus groups, we studied children with ITP, parents of children with ITP, and health care professionals, inquiring about participants' experience with decision support and decision making in newly diagnosed ITP. Data were examined using thematic analysis. Themes that emerged from children were feelings of "anxiety, fear, and confusion"; the need to "understand information"; and "treatment choice," the experience of which was age dependent. For parents, "anxiety, fear, and confusion" was a dominant theme; "treatment choice" revealed that participants felt directed toward intravenous immune globulin (IVIG) for initial treatment. For health care professionals, "comfort level" highlighted factors contributing to professionals' comfort with offering options; "assumptions" were made about parental desire for participation in shared decision making (SDM) and parental acceptance of treatment options; "providing information" was informative regarding modes of facilitating SDM; and "treatment choice" revealed a discrepancy between current practice (directed toward IVIG) and the ideal of SDM. At our center, families of children with newly diagnosed ITP are not experiencing SDM. Our findings support the implementation of SDM to facilitate patient-centered care for the management of pediatric ITP.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".