Comparison of severity index and plasma paraquat concentration for predicting survival after paraquat poisoning
Bibliographic record
Abstract
BACKGROUND: Severity index and plasma paraquat (PQ) concentration can predict the prognosis of patients with PQ poisoning. However, the better parameter is yet to be systematically investigated and determined. Thus, we conduct this systematic review and meta-analysis to investigate the prognostic value of severity index and plasma PQ concentration in patients with PQ poisoning. METHODS: We searched PubMed, Embase, Web of Science, ScienceDirect, and Cochrane Library to identify all relevant papers that were published up to March 2019. All diagnostic studies that compared severity index and plasma PQ concentration to predict mortality in patients with PQ poisoning were enrolled in this meta-analysis. Odds ratios (ORs) with 95% confidence intervals (CIs) for individual trials were pooled using a random-effect model. We also aggregated heterogeneity testing, sensitivity analysis, and publication bias analysis. RESULTS: Ultimately, seven studies involving 821 patients were included. The pooled OR with a 95% CI of severity index was 24.12 (95% CI: 9.34-62.34, P < .001), with an area under the curve of 0.88 (95% CI: 0.85-0.90), sensitivity of 0.84 (95% CI: 0.74-0.91), and specificity of 0.81 (95% CI: 0.75-0.87). Meanwhile, the pooled OR with 95% CI of plasma PQ concentration was 34.39 (95% CI: 14.69-80.56, P < .001), with an area under the curve of 0.94 (95% CI: 0.91-0.96), sensitivity of 0.86 (95% CI: 0.75-0.93), and specificity of 0.89 (95% CI: 0.76-0.95). Sensitivity analysis demonstrated the stability of the results of our meta-analysis. No significant publication bias was observed in this meta-analysis. CONCLUSION: Overall, this study indicated that severity index and plasma PQ concentration have relatively high-prognostic value in patients with PQ poisoning, and that the sensitivity and specificity of plasma PQ concentration are superior to those of severity index.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.035 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".