Conditional Treatment Effect Analysis of Two Infusion Rates for Fluid Challenges in Critically Ill Patients: A Secondary Analysis of Balanced Solution versus Saline in Intensive Care Study (BaSICS) Trial
Bibliographic record
Abstract
Abstract Rationale Optimal infusion rate for fluid challenges in critically ill patients is unknown. A large clinical trial comparing two different infusion rates yielded neutral results. Conditional average treatment effect (CATE) assessment may aid in tailoring therapy. Objectives To estimate CATE in patients enrolled in the BaSICS trial and to assess the effects of receiving CATE model–recommended treatment in terms of hospital mortality. Methods Post hoc analysis of the BaSICS trial assessing the effect of two infusion rates for the fluid challenge (fast, 999 ml/h, control group; vs. slow, 333 ml/h, intervention group) on hospital mortality. CATE was estimated as the difference in outcome for treatment arms in counterfactuals obtained from a Bayesian model trained in the first half of the trial adjusted for predictors hypothesized to interact with the intervention. The model recommended slow or fast infusion or made no recommendation in the second half. A threshold greater than 0.90 probability of benefit was considered. Results A total of 10,465 patients were analyzed. The model was trained in 5,230 patients and tested in 5,235 patients. A recommendation could be made in the test set in 19% of patients (14% were recommended the control group and 5% the treatment group); for 81% of patients, no recommendation could be made. Slow infusion was more frequently recommended in cases of planned admissions in younger patients; fast infusion was recommended for older patients with sepsis. Slow infusion rate in the subgroup of patients in the test set in which slow infusion was recommended by the model was associated with an odds ratio of 0.58 (95% credible interval of 0.32–0.90; 0.99 posterior probability of benefit) for hospital mortality. Fast infusion in the subgroup in which the model recommended fast infusion was associated with an odds ratio of 0.72 (credible intervals from 0.54 to 0.91; probability of benefit >0.99). Conclusions Estimation of CATEs from counterfactual probabilities in data from BaSICS provided additional information on trial data. Agreement between treatment recommendation and actual treatment was associated with lower hospital mortality. Clinical trial registered with clinicaltrials.gov (NCT 02875873).
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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 | 0.035 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".