The Correlation between Hypertropia and Head Tilt in Congenital Unilateral Superior Oblique Muscle Palsy
Bibliographic record
Abstract
Purpose: To evaluate the correlation between the angle of deviation in different gazes and the amount of head tilt in patients with congenital unilateral superior oblique muscle palsy (SOP). Methods: This case series study was performed on 20 consecutive SOP patients with head tilt. Based on the Bielschowsky three-step test, the angle of deviation was measured in different gazes. Furthermore, the hypertropia difference between the two lateral gazes (gaze difference) and the two head tilt sides (bilateral head tilt difference) was calculated. For measuring head tilt, close-up pictures from 40 cm with a habitual abnormal head position were captured and analyzed by the Corel Draw X7 software. Results: The mean age of patients was 13 ± 9 years (range, 2.5–31 years). The mean angle of hypertropia in ipsilateral and contralateral head tilt was 24.5 Δ ± 7.1 Δ and 6.5 Δ ± 4.2 Δ, respectively ( P < 0.001), and in ipsilateral and contralateral lateral gaze positions, it was 8.2 Δ ± 5.5 Δ and 22.5 Δ ± 6.1 Δ, respectively ( P < 0.001). The mean of bilateral head tilt hypertropia difference was 18 Δ ± 5.3 Δ and gaze hypertropia difference was 14.3 Δ ± 6.16 Δ. There was a positive correlation between bilateral head tilt hypertropia difference and the amount of head tilt ( R = 0.609, R 2 = 0.371, P = 0.004, the amount of head tilt = 0.39 × [Bilateral head tilt hypertropia difference] +1.77). The amount of head tilt also had a positive correlation with the gaze hypertropia difference ( R = 0.492, R 2 = 0.242, P = 0.028, the amount of head tilt = 0.27 × [gaze hypertropia difference] +4.81). Conclusion: In SOP patients, the amount of head tilt had a positive correlation with bilateral head tilt hypertropia difference and also gaze hypertropia difference.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".