Observational studies of early versus late salvage therapies in critical care exhibit intrinsic selection bias: two meta-analyses
Bibliographic record
Abstract
BACKGROUND: It is difficult to determine the optimal timing of salvage therapies, such as initiation of renal replacement therapies (RRT), using non-experimental designs. Therefore, using timing of RRT as a motivating example, we performed meta-analyses comparing observational and experimental studies assessing timing of RRT and timing of invasive mechanical ventilation (IMV). METHODS: We performed two meta-analyses of observational and experimental studies testing the association of early versus late initiation of RRT and IMV on mortality. RESULTS: We included 72 studies for RRT (57 observational, 15 experimental) and 50 for IMV (48 observational, 2 experimental). For RRT, observational studies showed mortality benefit with early RRT (OR 0.52, 95% CI 0.42-0.63) that was not seen in experimental studies (OR 0.94, 95% CI 0.76-1.17). For IMV, observational studies demonstrated harm with early IMV (OR 1.25, 95% CI 1.03-1.52), although not to the degree of experimental studies (OR 1.86, 95% CI 0.90-3.86). When observational studies were restricted to subjects who all received IMV, conclusions were further biased towards benefit favoring early IMV (OR 0.75, 95% CI 0.55-1.02). Studies that also included subjects who were never intubated showed harm with early IMV (OR 1.63, 95% CI 1.30-2.04). CONCLUSIONS: There were significant differences in the results of observational and experimental studies looking at timing of salvage therapies, partly due to selection bias in observational studies. This issue was worsened by only including subjects who receive the therapy. Randomized trials using objective eligibility criteria remain the best method to determine optimal timing of salvage therapies.
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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 | 0.099 | 0.175 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.070 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".