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Record W4206093763 · doi:10.4103/0028-3886.332245

Pediatric to Adult Hydrocephalus

2021· review· en· W4206093763 on OpenAlexaff
Manilyn Ann C. Hong, Arvind Sukumaran, Jay Riva-Cambrin

Bibliographic record

VenueNeurology India · 2021
Typereview
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicinePsychological interventionHydrocephalusHealth careAdult careEtiologyIntensive care medicinePediatricsNursingYoung adultGerontologyPsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: Pediatric patients treated for hydrocephalus, regardless of etiology, require continuous access to care to address the long-term sequelae from the disease progression itself and from the interventions undertaken. The challenge for all pediatric neurosurgeons is providing comprehensive and coordinated care for these patients in order to achieve a smooth and seamless transition into adult health care. METHODS: A review of the literature was conducted regarding the overall concept of pediatric patients with chronic conditions transitioning to adult care. We also specifically reviewed the pediatric hydrocephalus literature to investigate the barriers of transition, models of success, and specific elements required in a transition policy. RESULTS: The review identified several barriers that hamper smooth and successful transition from pediatric to adult care within the hydrocephalus population. These included patient-related, cultural/society-related, healthcare provider-related, and healthcare system-related barriers. Six elements for successful transitions were noted: transition policy, tracking and monitoring, transition readiness, transition planning, transfer of care, and transition completion stemming from the Got Transition center. CONCLUSIONS: A successful patient transition from pediatric neurosurgical care to adult neurosurgical care is very center-specific and depends on the available resources within that center's hospital, health system, and geo-economic environment. Six recommendations are made for transition policy implementation in resource-poor environments, including beginning the process early, preferably at age 14 years.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.109
GPT teacher head0.484
Teacher spread0.375 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2021
Admission routes1
Has abstractyes

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