Flicker electroretinogram in preterm infants
Bibliographic record
Abstract
Abstract Background Infants born prematurely are at risk of developing retinopathy of prematurity, which is associated with abnormalities in retinal function as measured using electroretinography. The aim of this study was to record non-invasive flicker electroretinograms (ERGs) in preterm infants and compare function of moderate and very or extremely preterm infants. Methods In this non-randomized, cross-sectional study, 40 moderate preterm (gestational age (GA) 34 0/7 to 36 6/7 weeks, Group A) and 40 very or extremely preterm infants (GA ≤ 31 weeks, Group B) were recruited for flicker ERG recording through closed eyelids using the RETeval® device and skin electrodes. Group A was tested within the first week of life and Group B between 34th and 37th week postmenstrual age. Flicker stimuli were presented at 28.3 Hz with stimulus levels of 3, 6, 12, 30 and 50 cd•s/m 2 . Primary endpoints were peak time (ms) and amplitude (µV). Results Flicker ERGs were recordable in most infants with the highest proportion of reproducible ERGs at 30 cd•s/m 2 . Amplitudes increased with stronger flicker stimulation, while peak times did not differ significantly between stimulus levels nor groups. Amplitudes were significantly greater in Group B at the strongest stimulus level (Mann-Whitney-U-Test=198.00, Z = 4.097, p = <0.001). Conclusions Feasibility of collecting flicker ERG data in most preterm infants was confirmed. We found no evidence of reduced retinal responses to flicker stimuli associated with extreme prematurity. Higher amplitudes in very and extremely preterm infants could indicate acceleration of retinal development following birth, triggered by visual stimulation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".