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Record W4309013166 · doi:10.3390/pediatric14040059

Short-Term Outcome of Rehabilitation Program with Hybrid Assistive Limb after Tendon Lengthening in Patients with Cerebral Palsy

2022· article· en· W4309013166 on OpenAlexaboutno aff
Mayumi Matsuda Kuroda, Hirotaka Mutsuzaki, Shogo Nakagawa, Kenichi Yoshikawa, Kazushi Takahashi, Yuki Mataki, Ryoko Takeuchi, Nobuaki Iwasaki, Masashi Yamazaki

Bibliographic record

VenuePediatric Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsCerebral palsyMedicineGaitRehabilitationGait trainingPhysical therapyPhysical medicine and rehabilitationAmbulatorySurgery

Abstract

fetched live from OpenAlex

In this study, we aimed to evaluate the short-term outcomes of a rehabilitation program with the Hybrid Assistive Limb® after soft tissue lengthening in young patients with cerebral palsy. We assessed six patients with cerebral palsy who underwent soft tissue surgery followed by gait training using the Hybrid Assistive Limb®. Clinical assessments were conducted preoperatively, before, immediately after, and at 1, 2, and 3 months after gait training. Gross Motor Function Measure was improved 5.93 ± 6.11% (mean ± standard deviation, p < 0.05), Canadian Occupational Performance Measure performance was improved 3.12 ± 1.53 points, and satisfaction was improved 3.80 ± 2.14 points (p < 0.05). The knee extension strength on the operated side was changed 7.75 ± 4.97 Nm after the intervention (p = 0.07). In ambulatory patients, gait speed was changed 8.37 ± 1.72 m/min, stride length was changed 10 ± 6.16 cm, and 6 min walking distance was changed 52 ± 16 m after the intervention. Training with the Hybrid Assistive Limb® may improve walking ability and clinical outcomes in young patients with cerebral palsy after soft tissue lengthening.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.005
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.008
GPT teacher head0.247
Teacher spread0.240 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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