The Association Between Visiting Intensivists and ICU Outcomes*
Bibliographic record
Abstract
OBJECTIVES: We hypothesized that intensivists unfamiliar with an ICU team and the context of that ICU would affect patient outcomes. We examined differences in mortality when ICU patients were admitted under intensivists routinely working in that ICU and compared with those admitted by intensivists familiar with an ICU elsewhere in the same hospital. DESIGN, SETTINGS, AND PATIENTS: A 5-year natural experimental crossover study involving patients admitted to four ICUs in a large U.K. teaching hospital. INTERVENTIONS: During a period of service reconfiguration, intensivists routinely rostered to work in one ICU worked in another of the hospital's four ICUs. "Home" intensivists were those who continued to work in their usual ICU; "visitor" intensivists were those who delivered care in an unfamiliar ICU. Patient data were obtained from electronic patient records to provide analysis on sex, age, admission Sequential Organ Failure Assessment score, date and time of admission, and admission type (elective, transfer, or unplanned). MEASUREMENTS AND MAIN RESULTS: We analyzed 9,981 admissions to four separate ICUs over a 5-year period. In total, 34.5% of patients were admitted by intensivists working in nonfamiliar surroundings. Visitor intensivists admitted patients with similar age and gender distributions but with greater physiologic derangement (mean Sequential Organ Failure Assessment score, 4.1 ± 2.8 vs 3.9 ± 2.8; p < 0.001) than home intensivists. Overall ICU mortality rates were higher in visitor intensivists, albeit not significantly so (11.5% vs 10.2%; p = 0.052). However, when the ICUs were analyzed separately, visitor mortality rates were found to be significantly higher than for home intensivists in two of the four ICUs (p = 0.017, 0.006). A multivariable analysis adjusting for confounding factors and the clustering of consultants revealed that the overall mortality rate was significantly higher for visitors (odds ratio, 1.18; 95% CI, 1.02-1.37; p = 0.024). A significant interaction between the ICU and visitor status was also detected (p = 0.046), with the visitor effect remaining significant in the two ICUs identified previously (both p = 0.009). CONCLUSIONS: Visitor intensivists in some ICUs were associated with higher mortality. The reasons are unknown but could relate to intensivists' practices, unfamiliarity with the patients, or the interaction with the interprofessional team.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".