Head Position–Dependent Changes in Ocular Torsion and Vertical Misalignment in Skew Deviation
Bibliographic record
Abstract
OBJECTIVES: To investigate whether ocular torsion and vertical misalignment differ in the upright vs supine position in skew deviation and to compare these findings with those in trochlear nerve palsy. METHODS: Ten patients with skew deviation, 14 patients with unilateral peripheral trochlear nerve palsy, and 12 healthy subjects were prospectively recruited. With subjects first in the upright position and then in the supine position, ocular torsion was measured by double Maddox rods and vertical misalignment was measured by the prism and alternate cover test. RESULTS: In patients with skew deviation, the abnormal torsion and vertical misalignment in the upright position decreased substantially with change to the supine position, whereas in patients with trochlear nerve palsy, it changed little between positions. Torsion was decreased by 83% in patients with skew deviation, 2% in patients with trochlear nerve palsy, and 6% in healthy subjects (P < .001). Similarly, vertical misalignment was decreased by 74% in patients with skew deviation and increased by 5% in patients with trochlear nerve palsy and 6% in healthy subjects (P < .001). CONCLUSIONS: Our findings provide the basis for additional clinical tests to support the classic 3-step test: ocular torsion and vertical misalignment that decrease from the upright position to the supine position indicate skew deviation, whereas torsion and vertical misalignment that do not change significantly between positions indicate trochlear nerve palsy.
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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 | 0.000 | 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) | 0.003 | 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".