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Record W4387966125 · doi:10.1017/cjn.2023.308

Building the Bridge From Pediatric to Adult Neurological Care

2023· article· en· W4387966125 on OpenAlexafffundvenueabout
Katherine Sawicka, Lindsey M. Vogt, Danielle M. Andrade, Hans Katzberg, Steven P. Miller, Mahendranath Moharir, David F. Tang‐Wai, Ana Marissa Lagman‐Bartolome

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2023
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsLondon Health Sciences CentreUniversity of TorontoWestern UniversityToronto Western HospitalUniversity of British ColumbiaPublic Health OntarioHospital for Sick ChildrenBC Children's HospitalToronto General HospitalUniversity Health NetworkSickKids FoundationInstitute for Clinical Evaluative Sciences
FundersUniversity of Toronto
KeywordsPediatric NeurologyNeurologyBridge (graph theory)Transition (genetics)Adult careFamily medicineMedicineHealth careNursingMedical educationPolitical sciencePediatricsGerontologyYoung adultPsychiatry

Abstract

fetched live from OpenAlex

ABSTRACT In this brief communication, we discuss the current landscape and unmet needs of pediatric to adult transition care in neurology. Optimizing transition care is a priority for patients, families, and providers with growing discussion in neurology. We also introduce the activities of the University of Toronto Pediatric-Adult Transition Working Group – a collaborative interdivisional and inter-subspeciality group of faculty, advanced-practice providers, trainees, and patient-family advisors pursuing collaboration with patients, families, and universities from across Canada. We envision that these efforts will result in a national neurology transition strategy that will inform designation of health authority attention and funding.

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)0.014
metaresearch head score (Gemma)0.022
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.053
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0090.007
Scholarly communication0.0100.012
Open science0.0030.026
Research integrity0.0070.019
Insufficient payload (model declined to judge)0.0280.005

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 head0.074
GPT teacher head0.371
Teacher spread0.296 · 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
GenreEmpirical

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

Citations3
Published2023
Admission routes4
Has abstractyes

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