Clinical and Soft-Tissue Computed Tomographic Predictors of Dysthyroid Optic Neuropathy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the ability to predict the presence of dysthyroid optic neuropathy (DON) using computed tomography assessment of soft-tissue and clinical features. STUDY DESIGN: A retrospective consecutive case series of patients with thyroid-related orbitopathy. RESULTS: One hundred eighty-nine orbits from 99 patients were evaluated. Statistically significant clinical predictors of DON on univariate analysis included a difference in intraocular pressure from primary gaze to upgaze (P = .02), the presence of lagophthalmos (P = .04), and inflammation as measured by the VISA (vision, inflammation, strabismus, appearance/exposure) inflammatory scale (P = .004). Dysthyroid optic neuropathy was inversely related to the marginal reflex distance (P = .01), levator function (P = .02), total ductions (P = .003), and interpalpebral fissure (P = .04). Statistically significant radiologic predictors determined on univariate analysis included apical crowding (P < .001), presence of enlarged tendons (P = .004), increasing total rectus diameter (P = .02), and presence of small, low densities within the recti muscles (P = .04). Multivariate analysis found only total ductions (P = .02) and marginal reflex distance (P = .04) determined on clinical examination and apical crowding shown on computed tomography (P = .003) to be significantly associated with DON. Receiver operating characteristic curves were used to evaluate the ability of the clinical and radiologic assessment, as well as the combination of these assessments, to predict DON. All 3 models were strong predictors of DON, with no statistically significant differences in the area under the receiver operating characteristic curve among them (P = .14). CONCLUSIONS: Total ductions, marginal reflex distance, and apical crowding observed on computed tomography scans are able to predict the presence of DON with high sensitivity, specificity, positive predictive value, and negative predictive value. Eyelid ptosis is a novel predictor of DON.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it