A look at literature on myopia over the past 25 years: a personal review
Bibliographic record
Abstract
Over 100 years ago, Professor Foucher of Université Laval in Montreal (Canada) suggested that myopia was the result of an interaction between genetics and the visual environment, implying a Darwinian response to our changing world. His words are still relevant today. Over 30,000 articles have been published since he spoke then. Have his questions been answered? What has been learned from this body of research, particularly in the last 25 years? The purpose of this paper is to review the scientific evidence on myopia and to give clinical significance to the results and conclusions presented. It is therefore not a conventional review. More specifically, this work covers the major trends that have characterised myopia research, allowing us to refine our understanding of the mechanisms leading to the onset and development of myopia and to assess the effectiveness of optical and pharmacological methods for its treatment. This is a clinically oriented text that helps to understand why the strategies used to treat myopia produce certain results but also highlights their limitations. It opens up new perspectives. Science has indeed answered many questions about myopia. But it has also raised many more that need to be addressed in future research notably to facilitate the most accurate prediction of the evolution of a particular individual and his or her response to a given strategy.
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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".