PREDICTING THE EFFECTIVENESS OF MYOPIA CONTROL WHEN USING ORTHOKERATOLOGICAL LENSES BASED ON INDIVIDUAL EYE PARAMETERS
Bibliographic record
Abstract
Introduction. The prevalence and progressive course of myopia is one of the most important medical and social problems worldwide. In recent years, in our country and abroad there is a tendency to increase the incidence of myopia, becoming in some countries an epidemic. In recent years, the most common method of effective control of myopia is the method of refractive therapy with orthokeratological lenses. Objective: To develop criteria for predicting the effectiveness of myopia control using orthokeratological lenses based on individual eye parameters. Object and methods of research. A total of 60 children (117 eyes) were included in the clinical study, who were selected by OKL of combined design, SkyOptix, licensed by KATT Design Group (Canada). The average age was 11 [10; 13], from 7 to 14 years, of which 37 were females (61.7%), 23 males (38.3%). Ophthalmological examination consisted of visometry without correction and with optical correction, autorefractometry on the narrow pupil and in the state of drug cycloplegia, biomicroscopy, biometry, ophthalmoscopy of the central and peripheral fundus, keratotopography of the horns, pupilometry. The results of the research: Among the studied people, the average refractive index at the beginning of the study was -2.25 [-3; -1.5] diopters. The initial diameter of the pupils was determined from 2.78 to 6.30 mm according to the pupilometry performed on the topograph. The average values of eccentricity (Ex) studied in the flat meridian averaged 0.51 [0.47; 0.58], in the steep - 0.53 [0.43; 0.59] at the beginning of the study. In our study, the keratometry of the cornea averaged 43.5 at the beginning of the study [42.7; 44.4]. As a result of probability analysis (increase in APS for two years), the method of construction of logistic regression models was used. We selected five factor features (X): Initial refraction, Pupil diameter (X2), Keratometry (X3), Peripheral corneal force on the reverse zone ring (X4 and X5), Corneal diameter, and Axial length of the eye (APS) at the time of treatment (initial). The equation of logistic regression of the model for predicting the probability of progression of myopia on the background of the use of OKL had the form: The classification ability of the model was determined according to the training sample and amounted to 79.5%. The probability of a true positive result (increase in APS less than 0.3) when using this model was 91.9%, and the probability of a true negative result - 38.5%. Evaluation of the quality of the model using ROC-analysis showed the following: the area under the ROC-curve (AUC) was equal to 0.86 (p <0.001), which characterizes the good quality of the classification of traits. The sensitivity of the model was 82.6%, specificity - 73.1%. As part of the research and mathematical calculations, an interactive WEB application was also developed, which can be accessed from various types of devices connected to the World Wide Web. Modern solutions and approaches used in IT (Information Technology) were used for development. Conclusions. It is determined that when examining a child with progressive myopia, it is important to pay attention to the diameter of the pupil in photographic conditions, because it can be a predictor of progression and influence the choice of correction individually. The smaller the value of keratometry before the appointment of refractive therapy, the greater the value of the size of the APS, so this factor can be indicated as prognostic. The differential topographic force of the cornea along the peripheral ring corresponding to the reverse zone of the lens is a prognostic practical factor. Taking into account the primary parameters of the eye allows you to customize the approach to each child with myopia, improving the individual design of orthokeratological lenses.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".