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Record W2901352240 · doi:10.1177/2329048x18811452

Functional Gains in Children With Spastic Hemiplegia Following a Tendon Achilles Lengthening Using Computerized Adaptive Testing—A Pilot Study

2018· article· en· W2901352240 on OpenAlexaff
Eli Saleh, Noémi Dahan‐Oliel, Kathleen Montpetit, Thierry E. Benaroch, Rita Yap, Nadia Barakat, M.J. Mulcahey

Bibliographic record

VenueChild Neurology Open · 2018
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsShriners Hospitals for Children - CanadaMcGill UniversityMontreal Children's HospitalUniversité de Montréal
FundersShriners Hospitals for Children
KeywordsCerebral palsyAchilles tendonPhysical medicine and rehabilitationMedicineGross Motor Function Classification SystemSpastic cerebral palsyPhysical therapyComputerized adaptive testingFunctional testingTendonSpasticSurgeryMEDLINEPsychometrics

Abstract

fetched live from OpenAlex

Purpose: This pilot study evaluated the outcomes of tendon Achilles lengthening in 12 children (mean age: 11.2 years) with spastic hemiplegia. Methods: Cerebral Palsy Computer Adaptive Tests, the timed up-and-go, the Gross Motor Function Measure, the Gillette Functional Assessment Questionnaire, and the Pediatric Outcomes Data Collection Instrument were administered at baseline and at 6, 12, and 24 months postsurgery. Results: Significant improvement at the latest follow-up (12-24 months following surgery) was seen in all domains of the Cerebral Palsy Computer Adaptive Test: activity ( P = .017), lower extremity ( P = .005), global ( P = .005), pain ( P = .005), and fatigue ( P = .028), as well as in the Gross Motor Function Measure-standing domain ( P = .02) and the mobility domain of the Pediatric Outcomes Data Collection Instrument ( P = .04). Conclusion: These findings indicate that the tendon Achilles lengthening improved functional outcome in these children as measured by tests of physical function, walking speed, and activity performance.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.090
GPT teacher head0.303
Teacher spread0.213 · 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 designNon-randomized trial
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

Citations9
Published2018
Admission routes1
Has abstractyes

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