Impact of Head Tilt on Optical Coherence Tomography Image Orientation
Bibliographic record
Abstract
PURPOSE: Head tilt can have an impact on the orientation of posterior pole images. We conducted this study to determine the effect of head tilt on image orientation measured by the fovea-Bruch's membrane opening (FoBMO) angle with optical coherence tomography (OCT) imaging. METHODS: The study included 56 healthy subjects with mean (range) age of 33 (18 to 61) years. The dominant eye was first determined. To measure head tilt, a smartphone with a built-in gyroscope was affixed to the subject's head with adjustable straps. OCT imaging was performed in both eyes (in randomized order) at 0, 5, and 10 degrees of head tilt in the direction of the imaged eye (ipsilateral head tilt), and then in the opposite direction (contralateral head tilt). For each image, the device software determined Bruch's membrane opening center and the foveal pit from which the FoBMO angle was derived. RESULTS: Thirty-eight (68%) subjects were right eye dominant and 18 (32%) were left eye dominant. Each 1 degree head tilt resulted in a mean change of 0.76 degree in the FoBMO angle (P<0.01), with no significant difference in effect between the 2 eyes (P=0.72). The magnitude of the effect increased from 5 to 10 degrees, and was similar for both ipsilateral and contralateral head tilt. Ocular dominance did not modulate the effect of head tilt (P=0.42). CONCLUSIONS: Head tilt significantly affects OCT image orientation as measured by the FoBMO angle, presumably because cyclotorsion is not fully compensatory. The magnitude and direction of the effect does not depend on the dominant eye.
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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 | 0.004 |
| 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) | 0.002 | 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".