Development and Validation of a Patient Symptom Questionnaire to Facilitate Early Diagnosis of Thyroid-Associated Orbitopathy in Graves' Disease
Bibliographic record
Abstract
BACKGROUND: To construct a patient-based symptom questionnaire to facilitate early referral of thyroid-associated orbitopathy (TAO) in Graves' hyperthyroidism (GH). METHODS: Phase I of our study involved developing a symptomatology-based questionnaire for the self-reporting of TAO symptoms in patients recently diagnosed with GH. Phase II involved administering the questionnaire along with a standard ophthalmic examination to a screening cohort of patients newly diagnosed with GH. Symptoms highly associated with the clinical diagnosis of TAO were used to construct a tool with the highest possible sensitivity. Phase III involved validation of this tool in a new cohort of patients recently diagnosed with GH. For each patient, the diagnosis of TAO was made by both a standardized orbital ophthalmic exam and the questionnaire. Results from the questionnaire were then compared to the clinical examination. RESULTS: The questionnaire was compared to the standardized examination and found to have a sensitivity of 0.76 and a specificity of 0.82 in the validation phase of the study. INTERPRETATION: This questionnaire may be a useful tool in clinical practice to allow identification of patients with TAO secondary to GH. Future studies using this questionnaire are needed to determine whether earlier identification and management of these patients is associated with reduced morbidity from TAO.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".