Deficient stereopsis in the normal population revisited: why current clinical stereo tests may not be adequate
Bibliographic record
Abstract
Abstract Significance statement Applied applications for occupational screening, clinical tests should assess sensitivity to the sign as well as the magnitude of disparity. Purpose To determine why the high incidence of stereo anomaly found using laboratory tests with polarity-based increment judgements (i.e., depth sign) is not reflected in clinical measurements that involve single-polarity incremental judgements (i.e., depth magnitude). Methods An iPod-based measurement that involved the detection of an oriented shape defined by a single polarity-depth increment within a random dot display was used. A staircase procedure was used to gather sufficient trials to derive a meaningful measure of variance for the measurement of stereopsis over a large disparity range. Forty-five adults with normal binocular vision (20 - 65 years old) and normal or corrected-to-normal (0 logMAR or better) monocular vision participated in this study. Results Observers’ stereo acuities ranged between 10 and 100 arc seconds, and were normally distributed on a log scale ( p = 0.90, 2-tailed Shapiro-Wilk test). The present results using a single polarity depth increment task (i.e., depth magnitude) show a similar distribution to those using a similar task using the Randot preschool stereo test on individuals between the ages of 19-35 using either the 4-book test (n = 33) or the 3-book test (n = 40), but very different results when the iPod test involved a polarity-based increment judgement (i.e., depth sign). Conclusions The present clinical stereo tests are based on magnitude judgements and are unable to detect the high percentage of stereo anomalous individuals in the normal population revealed using depth sign judgements.
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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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".