Does subclinical hypothyroidism affect the prognosis of patients with chronic systolic heart failure: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Chronic systolic heart failure (CSHF) is a significant health burden with high morbidity and mortality. The role of subclinical hypothyroidism (SCH) in the prognosis of CSHF patients remains a critical area of inquiry. This systematic review and meta-analysis aim to elucidate the impact of SCH on the prognosis of patients with CSHF. METHODS: Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, this meta-analysis employed a comprehensive search strategy across major databases including PubMed, Embase, Web of Science, and the Cochrane Library. The Patient, Intervention, Comparison, Outcome framework guided the inclusion of studies focusing on patients with CSHF, comparing those with and without SCH. Quality assessment was performed using the Newcastle-Ottawa scale. Statistical analyses assessed heterogeneity and publication bias, employing fixed-effect or random-effects models based on heterogeneity levels. RESULTS: From an initial pool of 1439 articles, 8 studies met the stringent inclusion criteria. These studies, conducted across diverse geographical regions, highlighted the relationship between SCH and all-cause mortality, cardiac events, and subgroup differences in CSHF patients. The meta-analysis revealed SCH as a significant risk factor for all-cause mortality (HR = 1.42) and cardiac events (HR = 1.46). Subgroup analysis indicated variability in risk based on region, sample size, age, and follow-up duration. Sensitivity analysis confirmed the stability of these findings, and publication bias assessment indicated symmetric funnel plot and nonsignificant Egger test results. CONCLUSIONS: SCH emerges as a predictive factor for all-cause mortality, cardiovascular events, and rehospitalization in CSHF patients. This finding underscores the importance of screening for SCH in CSHF patients, highlighting its potential role in improving patient prognosis.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.040 |
| Bibliometrics | 0.005 | 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) | 0.002 | 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".