Fusional Vergence differences between manual phoropter and automated phoropter
Bibliographic record
Abstract
Purpose: The use of automated phoropters is becoming common in ophthalmic clinics however "the clinical norms" utilized for evaluating vergences were obtained using the manual phoropter. We sought to investigate and compare the fusional vergence findings obtained with the automated phoropter (Nidek RT-5100) and the manual phoropter (Topcon). Methods: The study was conducted at the College of Optometry at Western University of Health Sciences, Pomona California where a total of 188 participants (optometry students) who were paired and individuals examined each other and performed vergence measurements. The vergence measurement was performed for both distance vision (20 feet) and near vision (40 centimeters) using the 1) manual phoropter 2) automated phoropter. The sequence of measurement was randomized. Results: A paired samples t-test was utilized to evaluate the vergence data of blur/ break and recovery was analyzed for each method using paired samples t-test. The mean values of blur/break and recovery was significantly different between the two phoropters at 20 feet p-values of 0.006, 0.013, and 0.002 respectively. At near distance (40 cms) convergence base out showed significant difference for recovery (p= < 0.0001) and divergence base in prism for break in fusional vergence (p=0.006). Conclusion: The vergence values obtained using an automated phoropter is significantly different when compared to values obtained using manual phoropter and the results obtained using these phoropters cannot be used interchangeably. Clinicians need to take this into account when making any clinical judgement involving any prism prescription. A new set of clinical norms might be needed as a clinical guideline when evaluating patients using automated phoropters. Meeting abstract presented at VSS 2017
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".