Prevalence and Clinical Characteristics of Pretibial Myxedema in Chinese Outpatients With Thyroid Diseases
Bibliographic record
Abstract
Background: There have been no reports on the prevalence of pretibial myxedema (PTM) in thyroid diseases in China. In order to resolve the problem, we retrospectively investigated Chinese outpatients with thyroid diseases and analyzed the clinical manifestation of PTM. Methods: All outpatients with thyroid diseases at Department of Nuclear Medicine from October 24, 2000 to November 11, 2006 were included in the investigation by eligible case criteria and screened by PTM diagnosis criteria. The fill-out forms of PTM had three main items which contained demographics, diagnosis of thyroid diseases, relationship among I-131 therapy, thyroid function and onset of PTM, and the clinical manifestation of PTM. Descriptive statistics of the data were performed with statistical software SPSS17.0. Results: The prevalence of PTM was 1.6% (728/44,646) in thyroid diseases, 1.7% in thyrotoxicosis, and 0.36% in Hashimoto thyroiditis, primary hypothyroidism and thyroid adenoma, respectively. The average age was 41.1 ± 11.9 years (15 - 78 years). The sex ratio was 1:3.7. Eighty-three percent of cases were Chinese farmers. The onset of PTM was 63.9% in euthyroidism, 22% in hyperthyroidism, 11.4% in hypothyroidism and 2.7% in unclear thyroid function. The course was 10 days to 10 years and its average was 37.8 ± 20.5 months. The clinical forms were 82.6% in non-pitting diffuse swelling, 12.4% in plaque/nodule, 2.7% in verruciform plaque, 1.6% in elephantiasis and 0.7% in tumorous. Conclusions: The prevalence of PTM with chronic and autoimmune features is 1.6% in thyroid diseases. Its onset is not related to thyroid dysfunction and it should be treated early. J Endocrinol Metab. 2015;5(4):250-255 doi: http://dx.doi.org/10.14740/jem306e
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".