Mechanisms of emmetropization and what might go wrong in myopia
Bibliographic record
Abstract
Studies in animal models and humans have shown that refractive state is optimized during postnatal development by a closed-loop negative feedback system that uses retinal image defocus as an error signal, a mechanism called emmetropization. The sensor to detect defocus and its sign resides in the retina itself. The retina and/or the retinal pigment epithelium (RPE) presumably releases biochemical messengers to change choroidal thickness and modulate the growth rates of the underlying sclera. A central question arises: if emmetropization operates as a closed-loop system, why does it not stop myopia development? Recent experiments in young human subjects have shown that (1) the emmetropic retina can perfectly distinguish between real positive defocus and simulated defocus, and trigger transient axial eye shortening or elongation, respectively. (2) Strikingly, the myopic retina has reduced ability to inhibit eye growth when positive defocus is imposed. (3) The bi-directional response of the emmetropic retina is elicited with low spatial frequency information below 8 cyc/deg, which makes it unlikely that optical higher-order aberrations play a role. (4) The retinal mechanism for the detection of the sign of defocus involves a comparison of defocus blur in the blue (S-cone) and red end of the spectrum (L + M-cones) but, again, the myopic retina is not responsive, at least not in short-term experiments. This suggests that it cannot fully trigger the inhibitory arm of the emmetropization feedback loop. As a result, with an open feedback loop, myopia development becomes "open-loop".
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".