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Record W2726167565 · doi:10.1111/epi.13832

Epilepsy: Transition from pediatric to adult care. Recommendations of the Ontario epilepsy implementation task force

2017· article· en· W2726167565 on OpenAlexaffabout
Danielle M. Andrade, Anne S. Bassett, Eduard Bercovici, Felippe Borlot, Esther Bui, Peter Camfield, Guida Quaglia Clozza, Eyal Cohen, Timothy Gofine, Lisa Graves, Jon Greenaway, Beverly Guttman, Maya Guttman‐Slater, Ayman Hassan, Megan Henze, Miriam Kaufman, Bernard Lawless, Hannah Lee, Lezlee Lindzon, Lysa Boissé Lomax, Mary Pat McAndrews, Dolly Menna‐Dack, Berge A. Minassian, Janice Mulligan, Rima Nabbout, Tracy Nejm, Mary Secco, Laurene Sellers, Michelle Shapiro, Marie Slegr, Rosie Smith, Péter Szatmári, Leeping Tao, Anastasia Vogt, Sharon Whiting, O. Carter Snead

Bibliographic record

VenueEpilepsia · 2017
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsChildren's Hospital of Eastern OntarioCARE CanadaSurrey Place CentreCollege of Physicians and Surgeons of OntarioNeurAxon (Canada)McMaster UniversityHealth Sciences CentreOntario Shores Centre for Mental Health SciencesMcMaster University Medical CentreCollege of Family Physicians of CanadaKingston General HospitalQueen's UniversityHolland Bloorview Kids Rehabilitation HospitalSt. Michael's HospitalThunder Bay Regional Health Sciences CentreHealth Sciences NorthErinoakKids Centre for Treatment and DevelopmentUniversity of SudburyHospital for Sick ChildrenUniversity of TorontoSudbury Regional HospitalCentre for Addiction and Mental HealthHamilton Health SciencesUniversity of OttawaDalhousie UniversityCanadian Patient Safety InstituteSickKids FoundationToronto Western Hospital
Fundersnot available
KeywordsPsychosocialEpilepsyMedicineFamily medicinePsychiatrySocial workMultidisciplinary approachHealth carePolitical science

Abstract

fetched live from OpenAlex

The transition from a pediatric to adult health care system is challenging for many youths with epilepsy and their families. Recently, the Ministry of Health and Long-Term Care of the Province of Ontario, Canada, created a transition working group (TWG) to develop recommendations for the transition process for patients with epilepsy in the Province of Ontario. Herein we present an executive summary of this work. The TWG was composed of a multidisciplinary group of pediatric and adult epileptologists, psychiatrists, and family doctors from academia and from the community; neurologists from the community; nurses and social workers from pediatric and adult epilepsy programs; adolescent medicine physician specialists; a team of physicians, nurses, and social workers dedicated to patients with complex care needs; a lawyer; an occupational therapist; representatives from community epilepsy agencies; patients with epilepsy; parents of patients with epilepsy and severe intellectual disability; and project managers. Three main areas were addressed: (1) Diagnosis and Management of Seizures; 2) Mental Health and Psychosocial Needs; and 3) Financial, Community, and Legal Supports. Although there are no systematic studies on the outcomes of transition programs, the impressions of the TWG are as follows. Teenagers at risk of poor transition should be identified early. The care coordination between pediatric and adult neurologists and other specialists should begin before the actual transfer. The transition period is the ideal time to rethink the diagnosis and repeat diagnostic testing where indicated (particularly genetic testing, which now can uncover more etiologies than when patients were initially evaluated many years ago). Some screening tests should be repeated after the move to the adult system. The seven steps proposed herein may facilitate transition, thereby promoting uninterrupted and adequate care for youth with epilepsy leaving the pediatric system.

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)0.017
metaresearch head score (Gemma)0.028
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: Other · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score0.389

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.028
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0040.004
Science and technology studies0.0060.003
Scholarly communication0.0050.004
Open science0.0060.006
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0070.002

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 head0.047
GPT teacher head0.409
Teacher spread0.362 · 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
GenreOther

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

Citations111
Published2017
Admission routes2
Has abstractyes

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