IMI – Myopia Control Reports Overview and Introduction
Bibliographic record
Abstract
With the growing prevalence of myopia, already at epidemic levels in some countries, there is an urgent need for new management approaches. However, with the increasing number of research publications on the topic of myopia control, there is also a clear necessity for agreement and guidance on key issues, including on how myopia should be defined and how interventions, validated by well-conducted clinical trials, should be appropriately and ethically applied. The International Myopia Institute (IMI) reports the critical review and synthesis of the research evidence to date, from animal models, genetics, clinical studies, and randomized controlled trials, by more than 85 multidisciplinary experts in the field, as the basis for the recommendations contained therein. As background to the need for myopia control, the risk factors for myopia onset and progression are reviewed. The seven generated reports are summarized: (1) Defining and Classifying Myopia, (2) Experimental Models of Emmetropization and Myopia, (3) Myopia Genetics, (4) Interventions for Myopia Onset and Progression, (5) Clinical Myopia Control Trials and Instrumentation, (6) Industry Guidelines and Ethical Considerations for Myopia Control, and (7) Clinical Myopia Management Guidelines.
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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.026 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.013 |
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".