Which Stereotest do You Use? A Survey Research Study in the British Isles, the United States and Canada
Bibliographic record
Abstract
A wide range of stereotests are available to measure stereopsis. Because each test has its own advantages and disadvantages, opinions differ on which is the preferred test to use in clinical practice. We conducted surveys comparing the use of stereotests in the British Isles and in the United States and Canada. Two online surveys were developed following consultation with eye care professionals, one for each geographical area. Both surveys included two questions on the frequency of use of different stereotests, two questions on best practice stereotests, and two questions on the usefulness of stereotests. Researchers made distinctions between appointments with children below or above 6 years old for respondents from the British Isles and below or above 5 years old for respondents from the Unites Stated and Canada. The surveys were distributed through professional organisations. We found Frisby to be the most used stereotest on the British Isles for both age groups. In the US and Canada, Titmus and Randot stereotest are more frequently used. Respondents consider these tests as the best practice stereotests. Eye care professionals agree stereotests are useful in the diagnosis and treatment decision making and even more so in obtaining an accurate measure of stereoacuity, especially with older children.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".