Prognostic role of red blood cell distribution width in patients with sepsis: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Outcome prediction for patients with sepsis may be conductive to early aggressive interventions. Numerous biomarkers and multiple scoring systems have been utilized in predicting outcomes, however, these tools were either expensive or inconvenient. We performed a meta-analysis to evaluate the prognostic role of red blood cell distribution width (RDW) in patients with sepsis. Methods The online databases of Embase, Web of science, Pubmed, Corchrane library, Chinese Wanfang database, CNKI database were systematically searched from the inception dates to June, 24th, 2020, using the keywords red cell distribution width and sepsis. The odds ratio (OR) or Hazards ratio (HR) with corresponding 95% confidence intervals (95%CI) were pooled to evaluate the association between baseline RDW and sepsis. A random-effects model was used to pool the data, and statistical heterogeneity between studies was evaluated using the I2 statistic. Sensitivity and subgroup analyses were performed to detect the publication bias and origin of heterogeneity. Results Eleven studies with 17,961 patients with sepsis were included in the meta-analysis. The pooled analyses indicated that increased baseline RDW was associated with mortality (HR = 1.14, 95%CI 1.09–1.20, Z = 5.78, P < 0.001) with significant heterogeneity (I2 = 80%, Pheterogeneity < 0.001). Similar results were found in the subgroup analysis stratified by site of infection, comorbidity, Newcastle-Ottawa Scale (NOS) score, study design, patients’ country. The predefined subgroup analysis showed that NOS score may be the origin of heterogeneity. Conclusions For patients with sepsis, baseline RDW may be a useful predictor of mortality, patients with increased RDW are more likely to have higher mortality.
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 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 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".