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Record W4247950613 · doi:10.1111/dmcn.38_13511

Novel method to measure spatial vision in brain‐injured children

2017· article· en· W4247950613 on OpenAlexaff
Marie‐Marthe Suner, Glen T. Prusky, J. Hill, Julie Carmel, Sharynne McLeod, Pradip Amin, Meridith Yohemas, S Langenberger, Jean-François Lemay

Bibliographic record

VenueDevelopmental Medicine & Child Neurology · 2017
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMeasure (data warehouse)PsychologyArtificial intelligenceComputer scienceNeuroscienceComputer visionPhysical medicine and rehabilitationMedicineData mining

Abstract

fetched live from OpenAlex

frequently used item features were positively worded items (i.e., avoided 'not') and self-evaluation (i.e., 'I can. ..' vs. 'My teacher thinks I can. ..').On average, PROMs included 4/5 form layout features (range: 3-4) and 2/9 administration features (range: 0-6).The most common form layout design features were legible font type and consistent layout across all items.Few PROMs included administration features, such as providing encouragement during completion or content individualization; these features may be critical to support access for youth with NDD with attention, cognitive, and communication impairments.Conclusions/Significance: Currently available pediatric PROMs of ADLs and IADLs incorporate limited accessibility design features.The observed lack of accessibility design features may threaten PROM validity for youth with NDD.Findings highlight the need to carefully consider PROM design features when selecting and developing PROMs for use in research or practice with youth with NDD.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.003

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 head0.025
GPT teacher head0.317
Teacher spread0.291 · 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 designBench or experimental
Domainnot available
GenreMethods

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
Published2017
Admission routes1
Has abstractyes

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