Assessment of Thyroid Function in Patients with Rheumatoid Arthritis inKunming, China: A Case-control Study
Bibliographic record
Abstract
INTRODUCTION: The present study aimed to analyze the prevalence of hypothyroidism in patients with rheumatoid arthritis (RA). In addition, the study aimed to elucidate the correlation of hypothyroidism with RA activity and to investigate the relationship between RA and thyroid dysfunction. MATERIALS AND METHODS: A total of 314 patients were categorized into two groups according to thyroid stimulating hormone (TSH) level: RA without hypothyroidism and RA with hypothyroidism. All patients underwent routine laboratory investigation, including thyroid function testing, and complete clinical assessment. These included the determination of the erythrocyte sedimentation rate as well as the level of TSH, free triiodothyronine, free thyroxine, total triiodothyronine level, total thyroxine level, C-reactive protein, rheumatoid factor immunoglobulin (RF-Ig), RF-IgA, RF-IgG, RF-IgM, cyclic citrullinated peptide immunoglobulin G (CCP IgG), complement component 3, and complement component 4. Based on these data, thyroid function, and rheumatoid factor levels were analyzed. RESULTS AND DISCUSSION: Curve estimation using linear regression revealed that CCP Ig level was significantly correlated with the TSH level (r=0.122, P=0.031). CONCLUSION: TSH level may be used as an auxiliary test to assess disease severity in patients with RA and to evaluate thyroid function. This evaluation parameter may be considered for determining clinical prognosis in patients with RA.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".