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Record W4321018494 · doi:10.15273/dmj.vol49no1.11641

A medical student’s guide to the slit lamp examination

2023· article· en· W4321018494 on OpenAlexaffvenue
Sunil Ruparelia

Bibliographic record

VenueDalhousie Medical Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Health Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSlit lampPrimary careMedicineCurriculumEye examinationEmergency departmentMedical educationEye careSlitOptometryOphthalmologyPsychologyFamily medicineNursingVisual acuityPedagogy

Abstract

fetched live from OpenAlex

The slit lamp is an essential tool for the diagnosis of common eye pathology. Despite many eye conditions presenting initially to primary care, medical students do not typically receive formal training with the slit lamp in standard medical education curriculum. This guide provides a consistent, systematic framework that may be used by students and clinicians when approaching a slit lamp examination. Additionally, suggestions intended to optimize examination outcomes are described. It is our hope that this guide serves to enhance medical student comfort and proficiency with eye examinations, be it in an ophthalmology clinic, primary care, or emergency department setting.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.073
Threshold uncertainty score0.243

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0730.068

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 head0.098
GPT teacher head0.530
Teacher spread0.432 · 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 designNot applicable
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

Citations2
Published2023
Admission routes2
Has abstractyes

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