Vection Responses in Patients With Early Glaucoma
Bibliographic record
Abstract
PURPOSE: Our lab has previously shown that patients with early glaucoma have longer vection latencies than controls. We attempted to explain this finding using a combined index of structure and function (CSFI), as proposed by Medeiros and colleagues. The CSFI estimates the proportion of retinal ganglion cell loss. METHODS: Roll and circular vection were evoked using a back-projected screen (experiment 1) and the Oculus Rift system (experiment 2). Vection latency and duration were measured using a button response box. In experiment 1, tilt angles were measured with a tilt sensor, whereas subjective tilt was determined using a joystick attached to a protractor. In experiment 2, subjective vection strength was rated on a 1 to 10 scale. These measurements were compared with the CSFI, which utilizes visual field and optical coherence tomography data. RESULTS: For experiment 1 we tested 22 patients (mean age, 70.3±6 y) with glaucoma and 18 controls (mean age, 54.6±9 y); and for experiment 2 we tested 24 patients (mean age, 71.1 ±5 y) and 23 controls (mean age 61.4±10 y), but not all patients experienced vection. In both experiments, vection latency was significantly longer for patients than for controls (smallest P=0.02). The CSFI was not related to vection latency, duration, or objective and subjective measures of vection strength (smallest P=0.06) in either experiment. CONCLUSIONS: Two experiments have replicated the finding that vection responses are longer in patients with glaucoma than in controls; however, the CSFI is not related to vection responses.
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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 | 0.000 | 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".