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Record W4392095963 · doi:10.3233/prm-230006

REhabilitation Approaches in CHildren with cerebellar mutism syndrome (REACH): An international cross-disciplinary survey study

2024· article· en· W4392095963 on OpenAlexaboutno aff
Sharyl Samargia-Grivette, Helen Hartley, Karin S. Walsh, Jurgen Lemiere, Allison d. Payne, Emma Litke, Ashley D. Knight

Bibliographic record

VenueJournal of Pediatric Rehabilitation Medicine · 2024
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsRehabilitationNeuropsychologyIntervention (counseling)PopulationPsychological interventionMultidisciplinary approachMedicineClinical trialPhysical medicine and rehabilitationPhysical therapyPsychologyMedical educationPsychiatryPathologyCognitionSocial scienceEnvironmental healthSociology

Abstract

fetched live from OpenAlex

OBJECTIVE: Pediatric cerebellar mutism syndrome (pCMS) can occur following resection of a posterior fossa tumor and, although some symptoms are transient, many result in long-lasting neurological deficits. A multi-disciplinary rehabilitation approach is often used in cases of pCMS; however, there have been no clinical trials to determine gold standards in rehabilitation practice in this population, which remains a research priority. The purpose of this study was to identify and compare intervention practices used in pCMS throughout the disciplines of occupational and physical therapy, speech-language pathology, and neuropsychology across geographic regions. METHODS: A 55-question e-survey was created by an international multidisciplinary research group made up of members of the Posterior Fossa Society and sent to rehabilitation professionals in pediatric neuro-oncology centers in the US, Canada, and Europe. RESULTS: Although some differences in the type of intervention used in pCMS were identified within each discipline, many of the targeted interventions including dose, frequency, and intensity were similar within disciplines across geographic regions. In addition, there were common themes identified across disciplines regarding challenges in the rehabilitation of this population. CONCLUSION: These results provide a foundation of current practices on which to build future intervention-based clinical trials.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation 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 score0.008
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.054
GPT teacher head0.347
Teacher spread0.293 · 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 teacher head, 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

Citations2
Published2024
Admission routes1
Has abstractyes

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