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Record W2766542704 · doi:10.1177/0883073818786567

Effects of Botulinum Toxin Treatment in Nonambulatory Children and Adolescents With Cerebral Palsy: Understanding Parents’ Perspectives

2018· article· en· W2766542704 on OpenAlexaff
Linda Nguyen, Briano Di Rezze, Ronit Mesterman, Peter Rosenbaum, Jan Willem Gorter

Bibliographic record

VenueJournal of Child Neurology · 2018
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCerebral palsyInternational Classification of Functioning, Disability and HealthHypertoniaPsychologyBotulinum toxinCategorizationQualitative researchDevelopmental psychologyMedicinePhysical therapyPhysical medicine and rehabilitationClinical psychologyRehabilitationPsychiatry

Abstract

fetched live from OpenAlex

Children and adolescents with cerebral palsy often receive botulinum toxin A (BoNT-A) to manage hypertonia. This qualitative study aimed to describe and categorize BoNT-A effects that parents observed using the WHO's International Classification of Functioning, Disability and Health (ICF) framework. An interpretive description methodology was used; semi-structured interviews were conducted with 15 parents of nonambulatory young people with cerebral palsy (mean age 10.2 years, SD 3.9, 7 males) who received BoNT-A. Parents reported BoNT-A effects on each ICF category. Through interpretive description, an overall theme emerged: "finding the right path to do what is best." Five subthemes included (1) Parents' hopes, (2) Parents' goals for their child, (3) Parents' learning what works, (4) Parents' reflections, and (5) Parents' destination. This study provides insights into parents' journeys of how they learned about BoNT-A effects in their child, which helped them to identify goals for future treatment.

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)0.007
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.005
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.238
Teacher spread0.229 · 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 designQualitative
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

Citations11
Published2018
Admission routes1
Has abstractyes

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