Waterloo Eye Study: Data Abstraction and Population Representation
Bibliographic record
Abstract
PURPOSE: To determine data quality in the Waterloo Eye Study (WatES) and compare the WatES age/sex distribution to the general population. METHODS: Six thousand three hundred ninety-seven clinic files were reviewed at the University of Waterloo, School of Optometry. Abstracted information included patient age, sex, presenting chief complaint, entering spectacle prescription, refraction, binocular vision, and disease data. Mean age and age distributions were determined for the entire study group and both sexes. These results were compared with Statistics Canada (2006) estimates and information on Canadian optometric practices. Inter- and intraabstractor reliability was determined through double entry of 425 and 50 files, respectively; the Cohen kappa statistic (K) was calculated for qualitative data and the intraclass correlation coefficient (ICC) for quantitative data. Availability of data within the files was determined through missing data rates. RESULTS: The age of the patients in the WatES ranged from 0.2 to 93.9 years (mean age, 42.5 years), with all age groups younger than 85 years well represented. Females comprised 54.1% and males 45.9% of the study group. There were more older patients (>65 years) and younger patients (<10 years) than in the population at large. K values were highest for demographic information (e.g., sex, 0.96) and averaged slightly less for most clinical data requiring some abstractor interpretation (0.71 to 1.00). The two lowest interabstractor values, migraine (0.41) and smoking (0.26), had low reporting frequencies and definition ambiguity between abstractors. Intraclass correlation coefficient values were >0.90 for all but one continuous data type. Missing data rates were <2% for all but near phoria, which was 7.4%. CONCLUSIONS: The WatES database includes patients from all age groups and both sexes. It provides a fair representation of optometric patients in Canada. Its large sample size, good interabstractor repeatability, and low missing data rates demonstrates sufficient data quality for future analysis.
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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 | 0.020 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".