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Record W2621026698 · doi:10.1017/cjn.2017.105

P.020 A novel scale for describing visual outcomes in patients following resection of lesions affecting the optic apparatus – Unified Visual Function Scale

2017· article· en· W2621026698 on OpenAlexaffvenue
Serge Makarenko, V Ye, Ryojo Akagami

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2017
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Eye Disorders
Canadian institutionsVancouver Biotech (Canada)
Fundersnot available
KeywordsVisual fieldVisual acuityScale (ratio)MedicineFunction (biology)ResectionOptometryComputer scienceSurgeryOphthalmology

Abstract

fetched live from OpenAlex

Background: Historically, description of patient visual acuity and visual field changes following intracranial procedures has been very rudimentary. Clinicians and researchers have relied on the use of vague descriptions like “improved”, “worsened”, and “unchanged” to describe outcomes following resections of tumours affecting the optic apparatus, which are difficult to quantify in a clinical setting. Methods: We present a novel way to describe a patient’s visual function as a combination of visual acuity and visual field assessment that is simple to use and can be used by surgeons, and researchers to gauge visual outcomes following tumour resection. Results: With our scale we were able to capture the overall visual change while being sensitive enough to define the overall quantity of improvement or worsening quantitatively, using categories that are clinically relevant and understandable. Conclusions: The implementation of pre- and post- operative assessment provides clinically relevant information for surgeons and is robust for routine use.

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)0.004
metaresearch head score (Gemma)0.005
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 score0.061
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.067
GPT teacher head0.333
Teacher spread0.266 · 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
Published2017
Admission routes2
Has abstractyes

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