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Record W2779017493 · doi:10.25011/cim.v40i6.29126

Pattern reversal visual evoked potentials in adults: variability with age

2017· article· en· W2779017493 on OpenAlexvenueno aff
Raja Sawaya, Helen Sawaya, Gilbert Youssef

Bibliographic record

VenueClinical and investigative medicine · 2017
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsAudiologyElectrophysiologyAge groupsCheckerboardMedicineEvoked potentialStimulationMathematicsInternal medicineDemography

Abstract

fetched live from OpenAlex

PURPOSE: Pattern reversal visual evoked potential (PRVEP) is an electrophysiological test for evaluating the visual pathway. This study measured the changes in the latencies and amplitudes of the PRVEP with age and gender in normal subjects. METHODS: Healthy participants (n=81; 162 total eyes), between the ages of 20 and 92 years were recruited for the study. Stimulation was performed monocularly with a high-contrast (>50%) black-white checkerboard pattern with a check size of 30° at a reversal rate of 2 Hz, a band-pass of 1-100 Hz, a sweep of 250 msec and an average of 150 stimulations in a dark room. Mean and standard deviations for three latencies (N75, P100 and N145) and the amplitude (N75-P100) for each decade were measured. RESULTS: There was a linear trend by age for all three latencies, indicating that the higher age groups had longer latencies. The latencies decreased in the 5th decade before increasing in the higher age groups. The amplitude of N75-P100 decreased with age. The P100 latencies were longer in males than females in all age groups and the difference increased with increasing age.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 science0.0000.000
Research integrity0.0000.000
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 head0.215
GPT teacher head0.410
Teacher spread0.196 · 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

Citations11
Published2017
Admission routes1
Has abstractyes

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