Bibliographic record
Abstract
BACKGROUND: Patient monitoring displays are designed to improve patient safety, and yet little is known about how anesthesiologists interact with these displays. Previous studies of clinician behavior used an observer in the operating room, which may have altered behavior. We describe a covert observation technique to determine how often and for how long anesthesiologists actually look at the monitoring display during different segments of the maintenance phase of anesthesia, and to determine whether this changed with more than 1 anesthesia provider or during concomitant activities such as reading. METHODS: Five staff anesthesiologists, 2 anesthesia fellows, 3 anesthesia residents, and 2 medical students were covertly videotaped across 10 dual anesthesia provider cases and 10 solo cases. Videotapes were later segmented (5 minutes postinduction [early maintenance], mid-maintenance, and immediately before the drapes came down [late maintenance]) and coded for looking behavior at the patient monitor, anesthesia chart, and other reading material. RESULTS: Anesthesiologists looked at the monitor in 1- to 2-second glances, performed frequently throughout the 3 segments of maintenance anesthesia. Overall, the patient monitor was looked at only 5 of the analyzed time, which is less than has previously been reported. Monitoring behavior was constant across the segments of maintenance anesthesia and was not significantly affected by the number of anesthesia providers or role (trainee vs. senior). In contrast, charting behavior and other reading material viewing changed significantly over the analyzed segments of maintenance anesthesia. CONCLUSIONS: The presence of "at-a-glance monitoring" has implications for the design of patient monitoring displays. Displays should be developed to optimize the information obtained from brief glances at the monitor.
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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 | 0.000 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".