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Record W4394812176 · doi:10.1002/mds.29794

Recommendations for the Management of Initial and Refractory Pediatric Status Dystonicus

2024· review· en· W4394812176 on OpenAlexaff
Lindsey M. Vogt, Kathryn Yang, Gabriel Tse, Vicente Quiroz, Zainab Zaman, Laura Wang, Rasha Srouji, Amy Tam, Elicia Estrella, Shannon Manzi, Alfonso Fasano, Weston T. Northam, Scellig Stone, Mahendranath Moharir, Hernán Gonorazky, Brian J. McAlvin, Monica E. Kleinman, Kerri L. LaRovere, Carolina Gorodetsky, Darius Ebrahimi‐Fakhari

Bibliographic record

VenueMovement Disorders · 2024
Typereview
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsOntario Brain InstituteUniversity of TorontoUniversity Health NetworkToronto Western HospitalHospital for Sick Children
Fundersnot available
KeywordsDystoniaTriageMedicineQuality of life (healthcare)GuidelineNeuromodulationIntensive care medicinePhysical therapyPsychiatryPathologyInternal medicine

Abstract

fetched live from OpenAlex

Status dystonicus is the most severe form of dystonia with life-threatening complications if not treated promptly. We present consensus recommendations for the initial management of acutely worsening dystonia (including pre-status dystonicus and status dystonicus), as well as refractory status dystonicus in children. This guideline provides a stepwise approach to assessment, triage, interdisciplinary treatment, and monitoring of status dystonicus. The clinical pathways aim to: (1) facilitate timely recognition/triage of worsening dystonia, (2) standardize supportive and dystonia-directed therapies, (3) provide structure for interdisciplinary cooperation, (4) integrate advances in genomics and neuromodulation, (5) enable multicenter quality improvement and research, and (6) improve outcomes. © 2024 International Parkinson and Movement Disorder Society.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.005

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 head0.064
GPT teacher head0.390
Teacher spread0.326 · 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

Citations24
Published2024
Admission routes1
Has abstractyes

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