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Record W2969552750 · doi:10.1016/j.ijsu.2019.08.019

A prognostic role for non-thyroidal illness syndrome in chronic renal failure:a systematic review and meta-analysis

2019· review· en· W2969552750 on OpenAlexaboutno aff
Huaiyu Xiong, Peijing Yan, Qiangru Huang, Tiankui Shuai, Jingjing Liu, Lei Zhu, Jiajü Lü, Xinyu Shi, Kehu Yang, Jian Liu

Bibliographic record

VenueInternational Journal of Surgery · 2019
Typereview
Languageen
FieldMedicine
TopicThyroid Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeta-analysisCochrane LibraryInternal medicineConfidence intervalPublication biasFunnel plotRelative riskRenal functionMEDLINECreatinine

Abstract

fetched live from OpenAlex

BACKGROUND: Chronic renal failure (CRF) is a serious disease that has become a burden on global and local economics and public health. In addition, non-thyroidal illness syndrome (NTIS) has become increasingly more prevalent in CRF patients. MATERIALS AND METHODS: A data search was conducted on the PubMed/Medline, Cochrane Library, Web of Science, Embase, and CBM databases to identify studies up to November 1st, 2018, that compared low T3 and normal T3 levels in patients with CRF. Data analysis was done by calculating the relative risks (RR) and 95% confidence intervals (95% CI) and continuous variables were described by weighted mean difference (WMD) and 95% CI. The efficacy outcomes included renal function and mortality. The Newcastle-Ottawa Scale and Agency for Healthcare Research and Quality scale were used to assess the quality of the cohort and cross-sectional studies, respectively. A funnel plot was used to identify publication bias. RESULTS: = 0.0%; P-heterogeneity = 0.498;P = 0.798). CONCLUSION: Low T3 had a greater impact on the short-term prognosis of patients with CRF than on the long-term prognosis. NTIS did not cause substantial kidney damage.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.487
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0090.005
Bibliometrics0.0010.000
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 head0.068
GPT teacher head0.353
Teacher spread0.284 · 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.

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

Citations28
Published2019
Admission routes1
Has abstractyes

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