Use of the electroretinogram in a paediatric hospital
Bibliographic record
Abstract
BACKGROUND: To review the use of electroretinography and identify common reasons for referral and diagnoses at a tertiary paediatric hospital. DESIGN: Retrospective cohort study. PARTICIPANTS: Three hundred and eighteen patients (male 195, female 123) aged <18 years with 388 electroretinograms were included. METHODS: All electroretinograms performed at the Royal Children's Hospital, Brisbane from 1998 to 2005 were reviewed. Normative data and electroretinograms from patients aged ≥18 years were excluded. MAIN OUTCOME MEASURES: Reasons for referral and diagnoses were determined from each patient's first electroretinogram. Concordance between the first electroretinogram diagnosis and clinical referral was reviewed to determine whether the electroretinogram was normal, inconclusive, confirmed, excluded, or changed the clinical diagnosis or provided a new diagnosis. RESULTS: The median age at the time of the first investigation was 3.78 years (range 2.6 weeks to 17.5 years). The most common reasons for referral were nystagmus (n = 93), decreased vision (n = 33) and sensorineural deafness (n = 29). After one electroretinogram, 51% were normal (n = 162) and 15% were inconclusive (n = 49). The most common electroretinography diagnosis was cone rod dystrophy. The first electroretinogram for each patient confirmed the clinical suspicion in 17.6% (n = 56) and excluded it in 23% (n = 73) of cases. The electroretinogram resulted in a changed diagnosis in 0.9% (n = 3) and provided a new diagnosis in 15.1% (n = 48). Overall, the first electroretinogram was considered useful in 85% cases (n = 269). CONCLUSIONS: Electroretinography is a valuable investigation for evaluating paediatric eye disease and in this series confirmed, excluded, changed or provided a new diagnosis in 85% of cases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".