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Record W4292416102 · doi:10.1002/mdc3.13549

Transition Services for Children and Young Adults with Movement Disorders: A Survey by the <scp>MDS</scp> Task Force on Pediatrics

2022· article· en· W4292416102 on OpenAlexaff
Amit Batla, Jean‐Pierre Lin, Jitendra Kumar Sahu, Jonathan W. Mink, Tamara Pringsheim, Emmanuel Roze, Manju A. Kurian, Victor S.C. Fung

Bibliographic record

VenueMovement Disorders Clinical Practice · 2022
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsStaffingMovement disordersService (business)Task forceTransition (genetics)MedicineFamily medicineTask (project management)PsychologyPsychiatryPediatricsNursingBusinessPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Background: There is currently very limited data related to transition services for movement disorders. Objectives: Movement Disorders Society (MDS) Task Force on Pediatrics conducted a survey of current provision of transition for young adults with movement disorders. Methods: The survey questionnaire was based on review of available evidence, with questions designed to capture service location, transition clinic structure, and core issues discussed. The questionnaire was digitalized as an online survey and sent to all members of the MDS. Results: Responses were received from a total of 252 MDS members representing 67 countries. Of the responders, 59% confirmed that they provided transition clinics for adolescents with movement disorders. Overall, there was some consensus regarding transition services in terms of patient age at transition, movement disorder etiologies, staffing the service, and medical/social issues discussed. Conclusion: This survey provides first-hand data of existing movement disorder transition services and provides useful insights on transition clinics.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.031
GPT teacher head0.388
Teacher spread0.357 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

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