Morphological characterization of retinal development from birth to adulthood via retinal thickness assessment in mice: A systematic review
Bibliographic record
Abstract
The morphology and thickness of the retinal layers are valuable biomarkers for retinal health and development. The retinal layers in mice are similar to those in humans; thus, a mouse is appropriate for studying the retina. The objectives of this systematic review were: (1) to describe normal retinal morphology quantitatively using retinal layer thickness measured from birth to age 6 months in healthy mice; and (2) to describe morphological changes in physiological retinal development over time using the longitudinal (in vivo) and cross-sectional (ex vivo) data from the included studies. A PubMed search was conducted for articles published from to 1980-2024 that included quantitative data. Prior to sexual maturity, an increase in the total retinal and inner plexiform layer thicknesses were observed, with a decrease in the inner nuclear layer thickness. After sexual maturity, an asymptotic decrease in thickness was observed up to age 6 months in all layers; during this period, no significant changes were observed in the outer nuclear layer or nerve fiber layer/ganglion cell layer complex. Potential sources of variability and inconsistency among the studies included differences in imaging modality, animal strain, measurement timing, and retinal segmentation/assignment techniques. These findings highlight the importance of including a control group in experimental designs and providing comparative data for further investigations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.001 |
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".