Estimating ICU Benefit: A Randomized Study of Physicians
Bibliographic record
Abstract
OBJECTIVES: The distinction between overuse and appropriate use of the ICU hinges on whether a patient would benefit from ICU care. We sought to test 1) whether physicians agree about which types of patients benefit from ICU care and 2) whether estimates of ICU benefit are influenced by factors unrelated to severity of illness. DESIGN: Randomized study. SETTING: Online vignettes. SUBJECTS: U.S. critical care physicians. INTERVENTIONS: Physicians were provided with eight vignettes of hypothetical patients. Each vignette had a single patient or hospital factor randomized across participants (four factors related and four unrelated to severity of illness). MEASUREMENTS AND MAIN RESULTS: The primary outcome was the estimate of ICU benefit, assessed with a 4-point Likert-type scale. In total, 1,223 of 8,792 physicians volunteered to participate (14% recruitment rate). Physician agreement of ICU benefit was poor (mean intraclass correlation coefficient for each vignette: 0.06; range: 0-0.18). There were no vignettes in which more than two thirds of physicians agreed about the extent to which a patient would benefit from ICU care. Increasing severity of illness resulted in greater estimated benefit of ICU care. Among factors unrelated to severity of illness, physicians felt ICU care was more beneficial when told one ICU bed was available than if ICU bed availability was unmentioned. Physicians felt ICU care was less beneficial when family was present than when family presence was unmentioned. The patient's age, but not race/ethnicity, also impacted estimates of ICU benefit. CONCLUSIONS: Estimates of ICU benefit are widely dissimilar and influenced by factors unrelated to severity of illness, potentially resulting in inconsistent allocation of ICU care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".