Differential visual acuity – A new approach to measuring visual acuity
Bibliographic record
Abstract
A novel type of acuity measurement, which we refer to as ‘differential acuity’, requires the observer to identify one unique target among three others which are identical. This is a proof of concept study aimed to determine if differential acuity is equivalent to standard measures of recognition acuity. To create a range of visual acuity, vision was optically blurred in sixteen adults with normal visual acuity. Visual acuity was then measured with the differential acuity targets in both crowded and uncrowded format, and compared with standard ETDRS acuity or with singly presented letters and uncrowded letters were analysed separately. The visual acuity results for crowded and uncrowded letters were analysed separately. Repeated measures analysis of variance showed that when a crowded Sloan C had to be differentiated from three crowded Os (CvsO), the results were not significantly different from ETDRS acuity or from naming one of four letters presented centrally (Name4) (p < 0.05). Similar results were found for uncrowded letters – the C versus O and Name4 gave similar visual acuity. The 95% limits of agreement between the naming and C versus O differential acuity measures were between 0.17 and 0.27 logMAR. From this proof of concept study we conclude that differential acuity gives similar results to the ETDRS chart in adults. We infer that the comparable but cognitively simpler differential visual acuity task could be applied in clinical settings for young children or patients with developmental delay who cannot respond by naming or matching. Un nuevo tipo de medición de la agudeza, al que denominaremos ‘agudeza diferencial, requiere que el observador identifique un único objetivo entre tres otros objetivos idénticos. Se trata de una prueba de estudio de concepto, que trata de determinar si la agudeza diferencial es equivalente a las mediciones estándar de agudeza de reconocimiento. Para crear un rango de agudeza visual, se degradó ópticamente la visión en dieciséis adultos con agudeza visual normal. A continuación se midió la agudeza visual con los objetivos de agudeza diferencial, tanto en formato aglomerado como no aglomerado, y comparándose con la agudeza ETDRS estándar, o con letras presentadas de manera única, analizándose las letras no aglomeradas separadamente. Los análisis de mediciones repetidas de varianza reflejaron que cuando una C Sloan aglomerada debía diferenciarse de tres O aglomeradas (CvsO), los resultados no eran significativamente diferentes de la agudeza ETDRS, o de nombrar una de las cuatro letras presentadas centralmente (Nombrar4) (p < 0,05). Se encontraron resultados similares para las letras no aglomeradas – C versus O – y Nombre4 arrojó una agudeza visual similar. Los límites de acuerdo del 95% de las mediciones de agudeza diferencial, entre nombrar y C versus O, se situaron entre 0,17 y 0,27 logMAR. A partir de este estudio de prueba de concepto concluimos que la agudeza diferencial arroja resultados similares al cuadro ETDRS en adultos. Podemos inferir que podría aplicarse la tarea de agudeza visual diferencial, comparable pero cognitivamente más simple, al entorno clínico para jóvenes o pacientes con retraso del desarrollo cognitivo, y que no pueden responder mediante denominación o emparejamiento.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".