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Record W2077108008 · doi:10.1002/mrm.22438

Reactions of young children to the MRI scanner environment

2010· article· en· W2077108008 on OpenAlexafffund
Krisztina L. Malisza, Toby L. Martin, Deborah Shiloff, Dickie Yu

Bibliographic record

VenueMagnetic Resonance in Medicine · 2010
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsSt.AmantResearch ManitobaUniversity of ManitobaNational Research Council Institute for Biodiagnostics
FundersCanadian Institutes of Health ResearchNovo Nordisk Fonden
KeywordsConfidence intervalMedicineStandard deviationPediatricsNuclear medicineMathematicsStatisticsInternal medicine

Abstract

fetched live from OpenAlex

Seventy children aged 2 to 7 years were exposed to the MRI environment through a series of steps typical of a research study. Their willingness to proceed through the process was used to estimate the prevalence of fear. Thirty-seven children (53%; 95% confidence interval [41%, 65%]) completed the approach sequence. Although the correlation of child age in months (Mean (M) = 60.1, standard deviation = 16.5, N = 70) and highest successful step (M = 5.8, standard deviation = 2.6, 95% confidence interval [5.2, 6.4]) completed was not statistically significant at the 0.05 level, r (68) = 0.21, P = 0.08, 95% confidence interval [-0.03, 0.42], the proportion of children aged 6-7 years who successfully completed all steps (14 of 21, 67%, 95% confidence interval [50%, 84%]) was significantly different from the proportion of children aged 2-3 years who completed all steps (six of 23, 26%, 95% confidence interval [11%, 41%]) (Fisher's exact test, two-tailed P = 0.0148). A failure rate of at least 50% should be included into group size calculations when performing studies with young children (2-7 years), in addition to motion and other experimental factors.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.015
GPT teacher head0.260
Teacher spread0.245 · 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

Citations27
Published2010
Admission routes2
Has abstractyes

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