Observational Study of Clinician Attentional Reserves (OSCAR): Acuity-Based Rounds Help Preserve Clinicians’ Attention
Bibliographic record
Abstract
OBJECTIVES: Team rounding in the ICU can tax clinicians' finite attentional resources. We hypothesized that a novel approach to rounding, where patients are seen in a decreasing order of acuity, would decrease attentional attrition. DESIGN: Prospective interventional internal-control cohort study in which stop signal task testing was used as a proxy for attentional reserves. Stop signal task is a measure of cognitive control and response inhibition in addition to performance monitoring, all reflective of executive control abilities, and our surrogate for attentional reserves. SETTING: The ICUs of Vanderbilt University Medical Center (site 1) and the University of Pennsylvania (site 2) from November 2014 to August 2017. SUBJECTS: Thirty-three clinicians at site 1, and 24 clinicians at site 2. INTERVENTIONS: Acuity-based rounding, in which clinicians round from highest to lowest acuity as determined by Sequential Organ Failure Assessment score or an equivalent acuity score. MEASUREMENTS AND MAIN RESULTS: The stop signal task results of ICU staff at two sites were compared for conventional (in room order) versus novel (in decreasing order of acuity) rounding order. At site 1, the difference in stop signal reaction time change between two rounding types was -39.0 ms (95% CI, -50.6 to -27.4 ms; p < 0.001), and at site 2, the performance stop signal reaction time was -15.6 ms (95% CI, -29.1 to -2.1 ms; p = 0.023). These sub-second changes, while small, are significant in the neuroscience domain. CONCLUSIONS: Rounding in decreasing order of patient acuity mitigated attrition in attentional reserves when compared with the traditional rounding method.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".