The Perceived Likelihood of Outcome of Critical Care Patients and Its Impact on Triage Decisions: A Case-Based Survey of Intensivists and Internists in a Canadian, Quaternary Care Hospital Network
Bibliographic record
Abstract
INTRODUCTION: There is high variability amongst physicians' assessments of appropriate ICU admissions, which may be based on potential assessments of benefit. We aimed to examine whether opinions over benefit of ICU admissions of critically ill medical inpatients differed based on physician specialty, namely intensivists and internists. MATERIALS AND METHODS: We carried out an anonymous, web-based questionnaire survey containing 5 typical ICU cases to all ICU physicians regardless of their base specialty as well as to all internists in 3 large teaching hospitals. For each case, we asked the participants to determine if the patient was an appropriate ICU admission and to assess different parameters (e.g. baseline function, likelihood of survival to ICU discharge, etc.). Agreement was measured using kappa values. RESULTS: 21 intensivists and 22 internists filled out the survey (response rate = 87.5% and 35% respectively). Predictions of likelihood of survival to ICU admission, hospital discharge and return to baseline were not significantly different between the two groups. However, agreement between individuals within each group was only slight to fair (kappa range = 0.09-0.22). There was no statistically significant difference in predicting ICU survival and prediction of survival to hospital discharge between both groups. The accuracy with which physicians predicted actual outcomes ranged between 35% and 100% and did not significantly differ between the two groups. A greater proportion of internists favoured non resuscitative measures (24.6% of intensivists and 46.9% internists [p = 0.002]). CONCLUSION: In a case-based survey, physician specialty base did not affect assessments of ICU admission benefit or accuracy in outcome prediction, but resulted in a statistically significant difference in level of care assignments. Of note, significant disagreement amongst individuals in each group was found.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".