The Montreal Experience: A Retrospective Study Part I—Basic Principles and Treatment Algorithm
Bibliographic record
Abstract
CONTEXT: Authors have refined myopia control strategies (MCS) from their experience treating more than 800 children who were followed at the Montreal School of Optometry Clinic (CUV). They developed a treatment algorithm known as the Montreal Experience (ME). Contrary to many other MCS, treatment modalities are selected after careful evaluation of a patient’s parameters (rate of progression, age of myopia onset, corneal parameters, pupil area), the risk factors for ocular pathology (growth charts), and taking into account the patient’s lifestyle and potential compliance. This represents a customized approach for each patient. PURPOSE: To evaluate the efficacy of MCS used following ME algorithm; the primary outcome relates to axial length progression over 24 months. METHODS: This is a retrospective study, conducted after approval of University IRB. Data were extracted from the file of each patient who: (1) consulted CUV between January 2017 and December 2018 and (2) were kept under the same MCS (same design/concentration). Clinical population is composed of 298 patients (35% Caucasian; 45% Asian; 20% others), with a median age of 11 (range 5–18). The treatment options were orthokeratology (OK-4 designs; N = 140), multifocal soft contact lenses (SMCL; 5 designs; N = 128), and low-dose atropine (LDA 0.01% to 0.25%; N = 42). RESULTS: Results are analyzed through sophisticated statistical models, designed for this purpose. At the end of a stepwise selection process that sequentially removed model terms that were not statistically significant, nine model terms remained: month, modality, the interaction of month and modality, refraction (SEQ), the interaction of SEQ and modality, gender, age, the interaction of age and month, and the interaction of age and modality. A total of 298 files were kept for analysis. Participant age varied from 9.7 to 12.5 years old. Baseline AL varied from 24.9 to 25.3 mm and SE refraction was −3.7 + 1.7 D on average. This study population was divided between Caucasian (34%), Asian (44%), and other ethnic origins (22%). Overall results indicate that results vary according to modality and months only. There is no statistical difference based on age, gender, and SEQ. All methods used were effective to slow the natural AL growth. Evolution was the lowest when using smaller treatment zones OK lenses (0.249 mm) and the highest (0.376 mm) for those treated with LDA. This OK advantage was statistically significant versus other modalities at 1 and 2 years. CONCLUSION: The Montreal Experience reveals that personalized MCS may be effective to manage myopia efficiently. It shows AL evolution comparable to the documented natural evolution of emmetropes, especially when using customized or smaller treatment zone OK lens design. Future work on other populations will confirm this tendency.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".