Study design, rationale and methods for a population‐based study of myopia in schoolchildren: the <scp>M</scp>yopia <scp>I</scp>nvestigation study in <scp>T</scp>aipei
Bibliographic record
Abstract
BACKGROUND: To describe the study design, rationale and methodology of the Myopia Investigation Study in Taipei (MIT). DESIGN: The MIT was a city-wide, population-based cohort study. PARTICIPANTS: Participants were grade 2 students (Fall 2013) of all 153 elementary schools in Taipei City. METHODS: The baseline data on the risk factors for myopia development was collected by parent-administered questionnaire surveys covering demographics, medical history, parental myopia, time spent on near work and outdoor activities, reading habits and eye care-seeking behaviour. Ocular examinations focused on the measurement of visual acuity (unaided and best-corrected) and refractive status (before and after cycloplegia), which will be carried out for the eligible schoolchildren biannually for 3 years consecutively. Once myopic children are identified, case manager-led telecoaching for health-care instructions and reminders will be delivered to parents or caregivers. MAIN OUTCOME MEASURES: To build a comprehensive database for prevalence, incidence and risk factors of early childhood myopia over a 3-year follow-up period. RESULTS: Of all 19 374 eight-year-old schoolchildren (10 210 [52.7%] boys) eligible for the MIT, 16 486 (85.1%) responded to the questionnaire, 12 019 (62.0%) were examined during the third quarter of 2013 and 11 590 (59.8%) (6267 [52.9%] boys) completed cycloplegic autorefraction on both eyes and were enrolled for further data analysis. There was no significant difference in terms of demographics between the analysed participants and all grade 2 students in Taipei City. CONCLUSIONS: Data from the MIT will provide population-based information concerning the prevalence, incidence and risk factors for myopia development among young schoolchildren in a metropolitan area of Taiwan.
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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 | 0.116 | 0.119 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".