Extraocular muscle enlargement in thyroid eye disease: systematic review and meta-analysis
Bibliographic record
Abstract
Objective While radiographic measurements of orbital structures are used to both diagnose thyroid eye disease (TED) and assess treatment response, the specific extraocular muscle (EOM) changes in TED have not been established. This study aims to systematically review and analyze the difference in EOM sizes in TED patients. Design Systematic review. Methods The full protocol was registered on PROSPERO (42024566103). The electronic databases EMBASE, Web of Science, and OVID MEDLINE were searched from inception to June 5, 2024, using keywords related to TED and EOMs. Studies were included if they reported EOM measurements in both a TED group and a control group . Data were analyzed using random-effects model meta-analyses, with subgroup analyses based on EOM measurement parameters (i.e., diameter, cross-sectional area, volume). Results Twenty-three studies were identified, which together included a total of 2 708 orbits with TED and 1 221 control orbits. Eleven studies were retrospective cohort studies, and 12 were prospective cohort studies. Meta-analysis revealed a mean difference between TED patients and controls in EOM diameter for the inferior rectus of 1.9 mm (95% confidence interval: 1.4–2.3; p < 0.01), medial rectus 1.6 mm (95% CI: 1.1–2.0; p < 0.01), lateral rectus 0.89 mm (95% CI: 0.4–1.4; p < 0.01), and superior rectus 1.3 mm (95% CI: 1.0–1.6; p < 0.01). The mean difference between TED patients and controls in EOM volume for the inferior rectus was 515 mm 3 (95% CI: 230–801; p < 0.01), superior rectus was 609 mm 3 (95% CI: 327–891; p < 0.01), medial rectus was 551 mm 3 (95% CI: 293–810; p < 0.01), and lateral rectus was 288 mm 3 (95% CI: 182–393; p < 0.01). Conclusions Across studies, there is an apparent enlargement of the inferior rectus in TED compared to controls, but the relative extent of involvement of the medial rectus, superior rectus, and lateral rectus is less consistent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.024 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".