Readiness for transition to adult care in adolescents and young adults with Turner syndrome
Bibliographic record
Abstract
Objectives Turner syndrome (TS) is a complex and chronic medical condition that requires lifelong subspecialty care. Effective transition preparation is needed for successful transfer from pediatric to adult care in order to avoid lapses in medical care, explore health issues such as fertility, and prepare caregivers as adolescents take over responsibility for their own care. The objective of this study was to evaluate accuracy of knowledge of personal medical history and screening guidelines in adolescents and young adults (AYA) with TS. Methods This was a prospective cross-sectional study of 35 AYA with TS of ages 13-22 years recruited from a tertiary care center. AYA completed questionnaires on personal medical history, knowledge of screening guidelines for TS, and the Transition Readiness Assessment Questionnaire (TRAQ). Results Eighty percent of AYA with TS were 100% accurate in reporting their personal medical history. Only one-third of AYA with TS were accurate about knowing screening guidelines for individuals with TS. Accuracy about knowing screening guidelines was significantly associated with TRAQ sum scores (r = 0.45, p < 0.05). However, there was no association between knowledge of personal medical history and TRAQ sum scores. Conclusions Transition readiness skills, TS-specific knowledge, and accurate awareness of health-care recommendations are related, yet distinct, constructs. Understanding of one's personal medical history is not an adequate surrogate for transition readiness. Validated tools for general transition, like the TRAQ, can be used but need to be complemented by TS-specific assessments and content. Providers are encouraged to identify opportunities for clinical and educational interventions well in advance of starting transfer to adult care.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".