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Record W4210564476 · doi:10.3390/ijerph19031767

Reliability Study of the Items of the Alberta Infant Motor Scale (AIMS) Using Kappa Analysis

2022· article· en· W4210564476 on OpenAlexaboutno aff
Jooyeon Ko, Hyun Kyoon Lim

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2022
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsnot available
Fundersnot available
KeywordsKappaInter-rater reliabilityIntra-rater reliabilitySupine positionSittingReliability (semiconductor)PsychologyCohen's kappaPhysical therapyMedicineStatisticsDevelopmental psychologyRating scaleMathematicsSurgeryPhysics

Abstract

fetched live from OpenAlex

PURPOSE: We evaluated the interrater and intrarater reliabilities of the Korean version of the Alberta Infant Motor Scale (K-AIMS). METHODS: For the interrater reliability test, six raters participated in the K-AIMS evaluation using video clips of 70 infants (aged between 0 and 18 months). One rater participated in an intrarater reliability test. Among 70 infants, 46 were born preterm and 24 were born full term. A total of 58 AIMS items were evaluated for supine, prone, sitting, and standing positions. A reliability analysis was conducted using ICC and Fleiss' kappa. RESULTS: The highest Fleiss' kappa was found for the 4-7 months group for sitting (K = 0.701-1.000) and standing (K = 0.721-1.000), while the lowest K was the 3 months or under group for standing (K = 0.153-1.000). We found higher Fleiss' kappa statistics when all infants were evaluated without grouping for the three positions (K = 0.727-1.000), except standing (K = 0.192-1.000), for the interrater analysis. CONCLUSION: Our results demonstrate the good reliability for the Korean version of the AIMS for Korean infants (preterm and full term).

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)0.003
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 score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0000.001
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 head0.050
GPT teacher head0.366
Teacher spread0.316 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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