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Record W3010981253 · doi:10.15446/rsap.v21n2.68702

Desarrollo motor de una cohorte retrospectiva de niños colombianos de hasta un año de edad corregida, según la escala motora infantil de Alberta

2019· article· es· W3010981253 on OpenAlexaboutno aff
Doris Valencia Valencia, E. Vega, Rodrigo Benavides-Nuñez

Bibliographic record

VenueRevista de Salud Pública · 2019
Typearticle
Languagees
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineScale (ratio)DemographyPediatricsCartographyGeography

Abstract

fetched live from OpenAlex

OBJECTIVES: The Alberta Infant Motor Scale is used worldwide to assess motor development in children under 18 months of age, both preterm and full-term. In Colombia, the scale is used, but there is little information on the results it yields. The objective of this study was to characterize a retrospective cohort of children under one year of age according to the Alberta scale to generate information about the results of its application in a Colombian population treated at a highly specialized hospital. METHODS: Descriptive, retrospective, cross-sectional study, in which the medical records of 411 children with corrected age between 0 and 12 months and a history of gestational age less than 40 weeks were evaluated. The Alberta scale was applied to all children between 2010 and 2016, and scores were analyzed statistically in a descriptive form. RESULTS: Most patients were classified by the scale as "normal development" as would be expected based on their medical history. The children in our sample had lower scores than those of the original Canadian sample at all ages. CONCLUSIONS: The scale was useful for screening normal children; however, the patients had lower scores when they were evaluated by the scale than in the original study, thus making evident the need to validate the scale in Colombia and generate reference curves.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.249
Teacher spread0.244 · 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.

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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

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