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Record W4200012941 · doi:10.1159/000520133

Assessment of the Ovarian Reserve by Serum Anti-Müllerian Hormone in Rheumatoid Arthritis Patients: A Systematic Review and Meta-Analysis

2021· review· en· W4200012941 on OpenAlexaboutno aff
Xianhui Zhang, Ying-an Zhang, Xin Chen, Pengyan Qiao, Liyun Zhang

Bibliographic record

VenueInternational Archives of Allergy and Immunology · 2021
Typereview
Languageen
FieldMedicine
TopicOvarian function and disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeta-analysisAnti-Müllerian hormoneRheumatoid arthritisInternal medicineOvarian reserveConfidence intervalCochrane LibraryBiomarkerHormonePregnancyInfertility

Abstract

fetched live from OpenAlex

BACKGROUND: The ovarian reserve has been reported to be diminished in patients with rheumatoid arthritis. However, these results are still controversial. Anti-Müllerian hormone (AMH) is considered a reliable biomarker for the ovarian reserve. We thus performed a meta-analysis to evaluate the AMH levels and the effect of DMARDs on the ovarian reserve in rheumatoid arthritis patients. METHODS: PubMed, EMBASE, the Cochrane Library, and 2 Chinese databases (CNKI and Wanfang database), up to September 2021, were searched for relevant studies. The Newcastle-Ottawa scale (NOS) was used to assess the quality of the included studies. Pooled standard mean difference (SMD) with 95% confidence intervals (CIs) were determined with the random-effects model. The heterogeneity was described by I2 statistic and p value from the Cochrane Q test. RESULTS: Eight eligible studies (679 patients and 1,460 controls) were included in the meta-analysis. Compared with healthy control, the AMH levels in RA patients were significantly lower with the pooled SMD of -0.40 (95% CI: -0.66 to -0.14). However, in comparison of AMH with and without DMARD treatment, there was no significant difference with the pooled SMD of -0.1 (95% CI: -0.39 to 0.19). CONCLUSION: The results indicated that there was an increased risk of ovarian failure in RA patients and which is not related to DMARD treatment.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.428
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.297
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations13
Published2021
Admission routes1
Has abstractyes

Explore more

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