An experimental study on the impact of clinical interruptions on simulated trainee performances of central venous catheterization
Bibliographic record
Abstract
Interruptions are common in the healthcare setting. This experimental study compares the effects of interruptions on simulated performances of central venous catheterization during a highly versus minimally complex portion of the task. Twenty-six residents were assigned to interruptions during tasks that are (1) highly complex: establishing ultrasound-guided venous access (experimental group, n = 15) or (2) minimally complex: skin cleansing (control group, n = 11). Primary outcomes were (a) performance scores at three time points measured with a validated checklist, (b) time spent on the respective tasks, and (c) number of attempts to establish venous access. Repeated measure analyses of variances of performance scores over time indicated no main effect of time or group. The interaction between time and group was significant: F (2, 44) = 4.28, p = 0.02, and partial eta 2 = 0.16, indicating a large effect size. The experimental group scores decreased steadily over time, while the control group scores increased with time. The experimental group required longer to access the vein (148 s; interquartile range (IQR) 60 to 361 vs. 44 s; IQR 27 to 133 s; p = 0.034). Median number of attempts to establish venous access was higher in the experimental group (2, IQR 1–7 vs. 1, IQR 1–2; p = 0.03). Interruptions during a highly complex task resulted in a consistent decrement in performance scores, longer time required to perform the task, and a higher number of venous access attempts than interruptions during a minimally complex tasks. We recommend avoiding interrupting trainees performing bedside procedures.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".