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Record W2334084351 · doi:10.1097/pep.0b013e3182a31704

Motor Development of Infants With Univentricular Heart at the Ages of 16 and 52 Weeks

2013· article· en· W2334084351 on OpenAlexaboutno aff
Irmeli Rajantie, Maarit Laurila, Kirsi Pollari, Tuula Lönnqvist, Anne Sarajuuri, Eero Jokinen, Esko Mälkiä

Bibliographic record

VenuePediatric Physical Therapy · 2013
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMotor skillMedicineHypoplastic left heart syndromePediatricsPopulationPhysical medicine and rehabilitationPhysical therapyCardiologyHeart diseasePsychiatry

Abstract

fetched live from OpenAlex

In Brief Purpose: To compare the motor development of patients with hypoplastic left heart syndrome (HLHS) and other types of univentricular heart (UVH) with peers who are healthy at the ages of 16 and 52 weeks. Methods: Motor development was assessed with the Alberta Infant Motor Scale (AIMS). Results: Both the 23 patients with HLHS and the 13 patients with UVH had lower total AIMS scores in both observations than the controls. At the age of 52 weeks, patients with HLHS had significantly lower scores in all 4 AIMS subscales, whereas patients with UVH had lower scores only in the prone and standing subscales. Conclusion: Motor development of patients with HLHS or UVH is delayed during the first year of life, especially in the prone and standing subscales. Motor development of infants with univentricular heart is delayed during the first year of life, especially on the prone and standing subscales of the AIMS. Although the AIMS was found to be a valid and reliable tool for this population, the authors believe more items assessing early standing skills would improve discrimination between typical and delayed motor development.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.251
Teacher spread0.241 · 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 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

Citations15
Published2013
Admission routes1
Has abstractyes

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