Is 25 Hz enough to accurately measure a dynamic change in the ocular accommodation?
Bibliographic record
Abstract
Accommodation is often recorded at a low sampling rate using devices such as autorefractors that are designed to measure the static refractive error. It is therefore important to determine if that resolution is sufficient to accurately measure the dynamic properties of accommodation. The current study provides both theoretical and empirical evidence on the ideal sampling rate necessary to measure a dynamic response. Accommodative and disaccommodative step stimuli ranging from 1–3D (1D steps) were presented using a Badal optical system. Responses from 12 children (8–13 years) and 6 adults (20–35 years) were recorded using a dynamic photorefractor (DPR). Fast Fourier transformation was applied to the unsmoothed dynamic responses including position, velocity and acceleration. Also, velocity and acceleration main sequence (MS) characteristics were compared between three photorefractor conditions on 3 subjects. The Nyquist sampling limit necessary to accurately estimate position, velocity and acceleration was at least 5, 10 and 70 Hz, respectively. Peak velocity and acceleration were significantly underestimated at a lower rate (p < 0.5). However, the slope of MS remained invariant with sampling rate (p > 0.5). Contrary to the previous findings, a dynamic accommodative response exhibited frequencies larger than 10 Hz. Stimulus direction and amplitude had no influence on the frequencies present in the dynamic response. Peak velocity and acceleration can be significantly underestimated when sampled at a lower rate. Taken as a whole, low sampling rate instruments can accurately estimate static accommodation, however, caution needs to be exercised when using them for dynamic accommodation. La acomodación se registra a menudo a una tasa de muestreo baja, utilizando dispositivos tales como los autorrefractómetros que están diseñados para medir el error refractivo estático. Por tanto, es importante determinar si dicha resolución es suficiente para medir con precisión las propiedades dinámicas de la acomodación. El estudio actual aporta evidencia tanto teórica como empírica acerca de la tasa de muestreo necesaria para medir una respuesta dinámica. Se presentaron estímulos de alteraciones de estimulación y relajación (desacomodación) de la acomodación que oscilaron entre 1 y 3D (pasos de 1D) utilizando un sistema óptico Badal. Se registraron las respuestas de 12 niños (de 8 a 13 años) y 6 adultos (de 20 a 35 años) utilizando un sistema de fotorrefracción dinámico (DPR). La transformación rápida de Fourier se aplicó a las respuestas dinámicas no uniformes incluyendo posición, velocidad y aceleración. También se compararon las características de la secuencia principal de velocidad y aceleración entre las tres situaciones del sistema de fotorrefracción en 3 sujetos. El límite de muestreo de Nyquist necesario para calcular con precisión la posición, velocidad y aceleración fue de al menos 5, 10 y 70 Hz respectivamente. La velocidad y aceleración máximas se subestimaron significativamente a una tasa inferior (p< 0,5). Sin embargo, la pendiente de la secuencia principal permaneció invariable con la tasa de muestreo (p>0,5). Contrariamente a los hallazgos anteriores, la respuesta acomodativa dinámica mostró unas frecuencias superiores a 10 Hz. La dirección y amplitud del estímulo no influyeron en las frecuencias presentes en la respuesta dinámica. La velocidad y aceleración máximas pueden subestimarse significativamente cuando se muestrean a una tasa menor. En conjunto, los instrumentos de baja tasa de muestreo pueden calcular con precisión la acomodación estática; sin embargo, debe actuarse con precaución a la hora de calcular la acomodación dinámica.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".