Association of thyroid hormones and thyroid-stimulating hormone with mortality in adults admitted to the intensive care unit: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Thyroid hormones (THs) and thyroid-stimulating hormone (TSH) seem to show high potential in predicting the clinical death outcome of patients admitted to the intensive care unit (ICU). However, diverse studies on this topic are conflicting. METHODS: A search was conducted by two investigators involved in this research in the PubMed, Embase, and Cochrane databases (all last launched on July 12, 2021). The quality of the included studies was evaluated using the Newcastle-Ottawa Quality Assessment Scale (NOS). Subgroup analyses were performed to determine the sources of heterogeneity. Sensitivity and publication bias analyses were also assessed. RESULTS: A total of 27 studies (4970 participants) were included based on the eligibility criteria. Compared with survivors, nonsurvivors were found to have lower levels of THs (T3, T4, fT3, and fT4), whereas no significant difference was found in TSH levels (13 studies for T3: standardized mean differences [SMD], -0.78; 95% CI, -1.36 to -0.20; I2 = 96%; p = 0.008; 11 studies for T4: SMD = -0.79; 95% CI, -1.31 to -0.28; I2 =95%; p = 0.0002; 14 studies for fT3: SMD = -0.76; 95% CI, -1.21 to -0.32; I2 = 95%; p = 0.0008; 17 studies for fT4: SMD = -0.60; 95% CI, -0.99 to -0.22; I2 = 95%; p = 0.002; 20 studies for TSH: SMD = 0.00; 93% CI, -0.29 to 0.29; I2 = 93%; p = 0.98). CONCLUSION: Nonsurvivors were associated with lower levels of THs (T3, T4, fT3, and fT4) than survivors. THs show great application potential in predicting ICU patients' death outcomes and improving already widely used prognostic scores in the ICU (ie, Acute Physiological and Chronic Health Evaluation [APACHE] II and Therapeutic Intervention Scoring System).
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".