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Are infants with torticollis at risk of a delay in early motor milestones compared with a control group of healthy infants?

2009· article· en· W2003458219 on OpenAlexaboutno aff
Anna Öhman, Staffan Nilsson, Anna‐Lena Lagerkvist, Eva Beckung

Bibliographic record

VenueDevelopmental Medicine & Child Neurology · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCraniofacial Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDevelopmental MilestonePediatricsMotor skillTorticollisSurgery

Abstract

fetched live from OpenAlex

Recently it has been claimed that infants with congenital muscular torticollis (CMT) are at risk of a delay in early motor milestones. The aim of the present study was to investigate whether infants with CMT are indeed at risk in comparison with a control group of healthy infants. A second aim was to investigate whether the time spent in a prone position and plagiocephaly had any influence on motor development. Eighty-two infants with CMT (35 females and 47 males) were compared with 40 healthy infants (18 females and 22 males). Motor development was assessed with the Alberta Infant Motor scale (AIMS). Multiple regression showed that infants in the CMT group had a significantly lower AIMS score than the control group at 2 months (p=0.03) and 6 months of age (p=0.05). Infants who spent at least three occasions daily in a prone position when awake had significantly higher AIMS scores than infants who spent less time prone at 2 months (p=0.001), 6 months (p<0.001), and 10 months of age (p<0.001). The CMT group achieved early motor milestones significantly later than the control group until the age of 10 months, but the risk of delay seems to be more strongly associated with little or no time prone when awake than with CMT.

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)0.003
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.005
GPT teacher head0.220
Teacher spread0.215 · 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

Citations76
Published2009
Admission routes1
Has abstractyes

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