Bibliographic record
Abstract
Obesity and sepsis are growing epidemics with significant morbidity and mortality. However, the association between obesity and the risk and outcomes of sepsis is inconsistent in the literature. In Aim 1 of my dissertation project, the systematic review and meta-analysis of 183,673 participants in 57 studies in the United States, Canada, Europe, Australia, Japan and Hong Kong, I found a J-shaped relationship between BMI and the risk of sepsis. Underweight, obese and morbidly obese patients were more likely to have sepsis as compared to normal weight patients. However, the result was only statistically significant in the comparison between morbidly obese patients and normal weight ones (OR, 1.76; 95% CI, 1.53 - 2.02). In Aim 2, with a large and nationally representative sample of over 1,000 hospitals in the US, we also found that obesity status was significantly associated with an increase in the prevalence of sepsis among hospitalized patients (OR, 1.18; 95% CI, 1.13 - 1.23). These results contribute to our growing body of evidence regarding the effect of obesity on infectious diseases. Given the fact that obesity is a modifiable factor, our finding might help to mobilize more efforts and resources for obesity prevention. Future studies should examine the mechanism through which obesity increases the risk of sepsis. In addition, more studies on the topic are recommended in developing countries to look into this issue in different settings. The systematic review and meta-analysis indicated that obesity was likely to be a protective factor against death from sepsis, but the effect size was small and not statistically significant (OR, 0.95; 95% CI, 0.72 - 1.25). However, in Aim 3 with a large and nationally representative sample of over 364,000 sepsis patients, we found that obesity was significantly associated with an 16% increased risk of sepsis mortality (OR, 0.84; 95% CI, 0.81 - 0.88) . This finding contributes to the emerging body of evidence regarding the obesity paradox. Future studies should examine the mechanism through which obesity reduces sepsis mortality so that new interventions might be implemented accordingly to increase survival of this fatal disease.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".