Predictors of time to remission and treatment failure in patients with Graves’ disease treated with propylthiouracil
Bibliographic record
Abstract
PURPOSE: Propylthiouracil is one of the thionamides used in the treatment of Graves' disease. The drug has serious side effects and long-term treatment might be needed to achieve remission. We designed this study to evaluate the clinical and thyroid Doppler characteristics that might predict time to remission and treatment failure in propylthiouracil treated Graves' patients. METHODS: 26 patients, among 134 presenting to our university hospital outpatient clinic between Feb -July 2007 and with first time diagnosis of clinical thyroid dysfunction, were clinically and ultasonographically diagnosed with Graves' disease. Doppler parameters, serum thyrotropin, free thyroxine and free triiodothyronine were measured at the beginning of the study and thyroid studies were repeated every 4 weeks until remission. Propylthiouracil 300 mg/day was started for each patient at the time of diagnosis and doses were titrated according to repeat thyroid studies. Patients were treated and followed up for 18 months. RESULTS: Treatment failure was associated with smoking (P = 0.001) and male gender (P= 0.037). Stepwise multiple regression analysis revealed that age, free thyroxine and superior thyroid artery flow rate were predictors of time to remission (P= 0.001, 0.002 and 0.003, respectively). CONCLUSION: The time to remission in Graves patients treated with propylthiouracil can be predicted using age, serum free thyroxine and superior thyroid artery flow rate. This may help early consideration of alternative treatment for the patients requiring prolonged treatment for remission or for those who fail medical treatment. This would decrease unnecessary, long-term propylthiouracil exposure with its serious side effects.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".