Tenon Recession: A Novel Adjunct to Improve Outcome in the Treatment of Large-Angle Strabismus in Thyroid Eye Disease
Bibliographic record
Abstract
PURPOSE: Treatment of large-angle strabismus in thyroid eye disease has historically suffered from low success rates. The authors report a novel technique that adds Tenon recession without conjunctival recession to standard extraocular muscle surgery. METHODS: Twenty-six patients with thyroid eye disease-associated strabismus with preoperative deviations of ≥ 25 prism diopters in horizontal or vertical deviations underwent strabismus surgery. The amount of recession for a given muscle was determined by a combined analysis of the preoperative versions, deviation in primary gaze and in gaze opposite the restricted muscle, and intraoperative forced duction testing. Before conjunctival closure, the underlying Tenon layer and associated orbital tissues were recessed and allowed to retract posteriorally. The overlying conjunctiva was subsequently reapproximated to its anatomic position without tension. RESULTS: The average preoperative measurements for horizontal and vertical deviations were 39.2 ± 13.4 and 45.0 ± 18.9 prism diopters, respectively. Average postoperative measurements for horizontal and vertical deviations were 3.2 ± 6.0 and 11.1 ± 11.9 prism diopters, respectively. Overall, 22 of 26 patients (84.6%) were within 10 prism diopters of intended correction postoperatively and achieved binocular fusion with or without the use of prisms for both distance and near vision. Four had residual deviations that required reoperation, all of which successfully produced binocular vision without the use of prisms. CONCLUSIONS: In the authors' experience, Tenon recession improves outcomes in surgery for large-angle strabismus in thyroid eye disease.
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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 | 0.000 |
| 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) | 0.001 | 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".