Health care transition in pediatric neurosurgery: a consensus statement from the American Society of Pediatric Neurosurgeons
Bibliographic record
Abstract
OBJECTIVE: The number of children with complex medical conditions surviving to adulthood is increasing. A planned transition to adult care systems is essential to the health maintenance of these patients. Guidance has been established for the general health care transition (HCT) from adolescence to adulthood. No formal assessment of the performance of pediatric neurosurgeons in HCT has been previously performed. No "best practice" for this process in pediatric neurosurgery currently exists. The authors pursued two goals in this paper: 1) define the current state of HCT in pediatric neurosurgery through a survey of the membership of the American Society of Pediatric Neurosurgeons (ASPN) on current methods of HCT, and 2) develop leadership-endorsed best-practice guidelines for HCT from pediatric to adult neurosurgical health care. METHODS: Completion of the Current Assessment of Health Care Transition Activities survey was requested of 178 North American pediatric neurosurgeons by using a web-based questionnaire to capture HCT practices of the ASPN membership. The authors concurrently conducted a PubMed/MEDLINE-based literature review of HCT for young adults with special health care needs, surgical conditions, and/or neurological conditions for the period from 1990 to 2018. Selected articles were assembled and reviewed by subject matter experts and members of the ASPN Quality, Safety, and Advocacy Committee. Best-practice recommendations were developed and subjected to peer review by external expert groups. RESULTS: Seventy-six responses to the survey (43%) were received, and 62 respondents (82%) answered all 12 questions. Scores of 1 (lowest possible score) were recorded by nearly 60% of respondents on transition policy, by almost 70% on transition tracking, by 85% on transition readiness, by at least 40% on transition planning as well as transfer of care, and by 53% on transition completion. Average responses on all core elements were < 2 on the established 4-point scale. Seven best-practice recommendations were developed and endorsed by the ASPN leadership. CONCLUSIONS: The majority of pediatric neurosurgeons have transition practices that are poor, do not meet the needs of patients and families, and should be improved. A structured approach to transition, local engagement with adult neurosurgical providers, and national partnerships between pediatric and adult neurosurgery organizations are suggested to address current gaps in HCT for patients served by pediatric neurosurgeons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".