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Record W4408185731 · doi:10.1038/s41598-025-91627-3

Monocular reading performance measured by MNREAD-P acuity chart in normo-readers schoolchildren

2025· article· en· W4408185731 on OpenAlexaff
Yan Jonathan D' Almeida Souza, Arthur Gustavo Fernandes, Nívea Nunes Ferraz

Bibliographic record

VenueScientific Reports · 2025
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsChartReading (process)MonocularVisual acuityComputer scienceOptometryOphthalmologyMedicineArtificial intelligenceMathematicsStatisticsLinguistics

Abstract

fetched live from OpenAlex

This study aimed to determine and establish values for reading performance in normo-readers schoolchildren. Literate students aged 8 to 18 years old without visual impairments were recruited. The MNREAD-P chart was used to assess monocular reading acuity (RA in logMAR), critical print size (CPS in logMAR), and maximum reading speed (MRS in words per minute - wpm). Participants were grouped into three education levels: Elementary School I (3rd to 5th grade), Elementary School II (6 to 9th), and High School (10 to 12th). Lower and upper normal limits were determined using a 95% confidence interval around the mean. The association of RA, MRS, and CPS with sex, heterophoria, and education level was analyzed with a significance level of p ≤ 0.05. The final sample included 184 eyes from 92 participants (65% female) with a mean age of 12.8 ± 2.9 years. The normative lower limits were: (a) RA of 0.1 logMAR for Elementary I and II and 0.0 logMAR for High School; (b) CPS of 0.3 logMAR for all education levels; (c) MSR of 116 wpm for Elementary I, 159 wpm for Elementary II and 192 wpm for High School. Higher education level was significantly associated with improvement in RA (p = 0.017) and MRS (p < 0.001).

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)0.002
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 score0.101
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.276
Teacher spread0.264 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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