Does rituximab improve clinical outcomes of patients with thyroid-associated ophthalmopathy? A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The current therapies of thyroid-associated ophthalmopathy (TAO) were still a challenging matter. In this study, we aimed to contrast the impact of before- after rituximab (RTX) therapy in the patients with TAO. METHODS: We searched the PubMed, EMBASE, and SCOPUS databases for articles published up to July 3, 2017. Fixed- or random-effects meta-analysis was used to provide pooled estimates of standard mean difference (SMD) both the primary outcome from clinical activity score (CAS), and secondary outcomes from thyrotropin receptor antibody (TRAb), proptosis, thyroid stimulating hormone (TSH), and interleukin-6 (IL-6) levels. In addition, the quality and each study was assessed using either the Newcastle Ottawa Scale (NOS) or the Cochrane Risk of Bias tool, and reliability of the meta-analytic result using the Grading of Recommendations Assessment, Development and Evaluation (GRADE). RESULTS: Of the 839 articles initially searched, 11 studies were finally eligible for inclusion. Subgroup analysis results showed that comparing with initial value, there was a decline in CAS at 1,3,6,12 month after RTX treatment, decreased TRAbs level at 6,12 month, proptosis improvement at least 1 month, unchanged IL-6 level at 6 month, decreased TSH level at 3 month but unchanged at 12 month. All included studies were classified as good quality. CONCLUSIONS: The pooled data suggested that the preliminary effects of RTX treatment on TAO might be promising. However, more large-sample and high-quality studies targeting RTX use during this disease and long-term surveillance of prognosis are urgently needed.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".