Adaptations and Deficits in the Vestibulo‐Ocular Reflex after Peripheral Ocular Motor Palsies
Bibliographic record
Abstract
Palsy of a nerve might be expected to lower vestibulo-ocular reflex (VOR) responses in its fields of motion, but effects of peripheral neuromuscular disease were unknown. We recorded the VOR during sinusoidal head rotations in yaw, pitch, and roll at 0.5-2 Hz and static torsional gain in 43 patients with unilateral nerve palsies. Sixth nerve palsy (n = 21) reduced both abduction and adduction VOR gains in darkness. In light, horizontal visually enhanced VOR (VVOR) gains were normal in moderate and mild palsy. In severe palsy, horizontal VVOR gains remained low in the paretic eye when it was fixating, whereas gains in the nonparetic eye became higher than normal. Third nerve palsy (n = 10) decreased VOR and VVOR gains during abduction, adduction, elevation, depression, extorsion, and intorsion. Fourth nerve palsy (n = 13) reduced VOR gains of the paretic eye during intorsion, extorsion, elevation, depression, abduction, and adduction, but in light vertical and horizontal VVOR gains were normal. In the nonparetic eye, all gains were normal. Reduced VOR gains in the direction of paretic muscles and also in the direction of their antagonists, together with normal gains in the nonparetic eye, indicate a selective adjustment to the antagonists of paretic muscles. Increase of VVOR gains to normal in the paretic eye, when used for fixation, without conjugate increase in gains in the occluded nonparetic eye, provides further evidence of selective adaptation for the paretic eye. Motions of the eyes after nerve palsies indicate monocular VOR adaptation in three dimensions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".