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Record W2605662819 · doi:10.1017/cjn.2015.82

CACN Platform Presentations

2015· article· en· W2605662819 on OpenAlexaffvenue
MM Yang, Ash Singhal, JM Kerr, AR Hengel, Shane George, Paul Thiessen, Katherine Muir, Paul Enarson

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2015
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Health Research
Canadian institutionsVancouver Biotech (Canada)Calgary Laboratory Services
Fundersnot available
KeywordsMedicineNeurologyRandomized controlled trialNeurological examinationFundus (uterus)Likert scalePhysical therapyOphthalmologySurgeryPsychology

Abstract

fetched live from OpenAlex

Background: Fundoscopy is an important component of the neurological examination, but can be challenging in uncooperative children. This study explores whether playing a video during eye examination, improves the success, duration and ease of pediatric fundoscopy. Methods: We completed a prospective, multi-clinic, block-randomized trial. Patients 1-4 years were recruited in the emergency department, neurology, spinal cord and general pediatric clinic. Patients were randomized (by eye examined) to video/non-video assisted fundoscopy. Successful exams were defined as visualizing the fundus within 60 seconds. Time to visualize optic disc was recorded and difficulty of exam was examined using a 10-point Likert scale. Results: 101 subjects were recruited, with a mean age of 2.8 years. Overall, there was a 20% absolute improvement in the success rate of visualizing the optic disc in the video versus non-video group (p<0.01). Time to visualize optic disc was also improved (Δ5.3s, p<0.01). Improvement in ease of examination with video were noted by caregivers and practitioners (p<0.01). Conclusion: Playing a video improved the ease, duration and most importantly the success of fundoscopy in younger children. This simple, inexpensive adjunct has great potential to improve the ease and efficacy of this aspect of the neurological examination.

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)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.007
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.282
GPT teacher head0.466
Teacher spread0.184 · 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; both teacher heads agree on what is shown here.

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
Published2015
Admission routes2
Has abstractyes

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