Prevalence of asymptomatic ocular conditions in subjects with refractive-based symptoms
Bibliographic record
Abstract
This study aims to determine the overall prevalence of ocular conditions in a population from 19 to 64 years old, presenting with refractive-based symptoms only. Results could impact clinical standard of eye care on a similar population. This is a retrospective study on patients seen for an eye examination at the Clinique Universitaire de la Vision (CUV), between January 2007 and 2009. Files of individuals who presented with refractive symptoms were only selected and classified by file number. Then, every third file from the beginning was kept and reviewed by a reader. A second reader did the same with every third file from the end. Both readers were trained to use the same analysis grid to classify the diagnosed ocular conditions. In the case of multiple findings, the most severe condition was considered. The overall prevalence of ocular conditions was determined by calculating their occurrence divided by the number of files analyzed. A total of 860 charts were analyzed. In 26.1% of the cases an ocular condition was diagnosed. This work establishes a higher prevalence of ocular conditions compared to another study conducted in Canada in the past. This difference can be explained by a different analytical methodology and by the fact that all examinations, in this study, were made under pupillary dilation. The presence of ocular conditions in 26% of asymptomatic patients supports the need to assess ocular health under pupil dilation as part of any eye examination. However, further cost-to-benefit analysis is required before establishing such a recommendation. Este estudio trata de determinar la prevalencia general de las condiciones patológicas oculares en una población de pacientes de 19 a 64 años de edad, que presentaron únicamente síntomas refractivos. Los resultados podrían suponer un impacto para los estándares clínicos de cuidado ocular en poblaciones similares. Este es un estudio retrospectivo sobre pacientes examinados en la Clinique Universitaire de la Vision (CUV), entre Enero de 2007 y 2009. Se seleccionaron y clasificaron por número de archivo aquellas historias de pacientes con síntomas refractivos únicamente. A continuación se seleccionó cada tercer archivo contando desde el inicio, el cual fue revisado por un mismo lector. Un segundo lector realizó la misma operación con cada tercer archivo contado desde el final. Ambos lectores fueron formados para utilizar la misma cuadrícula analítica para clasificar las condiciones oculares diagnosticadas. En caso de múltiples hallazgos se consideró la situación más severa. Se determinó la prevalencia general de las condiciones oculares mediante el cálculo de su ocurrencia, dividida por el número de archivos analizados. Se analizó un total de 860 historias. En el 26,1% de los casos se diagnosticó una condición patológica ocular. Este trabajo establece una mayor prevalencia de las condiciones patológicas oculares en comparación a otro estudio realizado en Canadá en el pasado. Esta diferencia puede explicarse por el uso de una metodología analítica diferente y por el hecho de que todos los exámenes de este estudio se realizaron en condiciones de dilatación de la pupila. La presencia de condiciones patológicas oculares en el 26% de los pacientes asintomáticos apoya la necesidad de evaluar la salud ocular como parte de cualquier examen ocular. Sin embargo, se hace necesario un análisis adicional coste-beneficio antes de establecer dicha recomendación.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".