Bibliographic record
Abstract
This thesis sought to explore and describe the occurrence of long-term cardiovascular outcomes in adult sepsis survivors. The work can be summarized, and will be presented, within three main domains. First, we described the association between surviving sepsis and long-term cardiovascular outcomes. Sepsis is a leading cause of morbidity and mortality worldwide. The outcome burden experienced by adults who survive a hospitalization for sepsis has gained increasing awareness but has yet to be well described. Using the methods of clinical epidemiology, we conducted a population-based cohort study and found that survivors of a sepsis hospitalization experienced a higher hazard of long-term cardiovascular outcomes compared to survivors of a non-sepsis hospitalization (hazard ratio: 1.30; 95% confidence interval: 1.27 – 1.32). This aim used several different methodological approaches, including matching and propensity score methods, and probabilistic bias analyses. Second, we described the risk factors for developing long-term cardiovascular outcomes after sepsis. Using a population-based cohort study, we identified both classic cardiovascular risk factors (e.g., age, sex, hypertension) and also characteristics of the episode of sepsis (e.g., site of infection, acute kidney injury, and a high troponin value) as important predictors of experiencing major cardiovascular outcomes after hospital discharge. This aim explored several sophisticated strategies to deal with the competing risk of death during the assessment of long-term outcomes in critical care survivors and advanced methods such as multiple imputation with chained equations to deal with missing data. Third, we emulated a potential future randomized controlled trial to evaluate therapies that might reduce the risk of experiencing major cardiovascular outcomes after sepsis. Specifically, we tested whether the prescription of renin-angiotensin system inhibitors after an episode of sepsis might reduce major cardiovascular outcomes during long-term follow-up. We found that compared to an active comparator (i.e., a calcium channel blocker or a thiazide diuretic), a new prescription for a renin-angiotensin system inhibitor reduced major cardiovascular outcomes among adult sepsis survivors without pre-existing cardiovascular disease (hazard ratio: 0.93; 95% confidence interval: 0.87 – 0.99). This aim used several advanced methodological approaches, including explicit target trial emulation, use of marginal structural models, and a Bayesian framework for causal inference.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".