Evaluation of tactile cues for simulated patients’ status under high and low workload
Bibliographic record
Abstract
The intensive care unit (ICU) is one of the most complex areas in hospital care, as patients require continuous monitoring by physicians and nurses. Currently, clinicians are informed about the patients’ physiological conditions through visual color-coded signals and auditory alarms. Previous studies have shown that vibrotactile cues can be used to inform clinicians of a patient’s vital signs status, either in a unisensory or multisensory alarm scheme. We present the results of the first in a series of experiments devoted to examining the feasibility to use tactile cues to convey detailed physiological information about more than one patient, rendered through a lower-leg tactile interface. The current experiment utilized a simulated clinical environment with 14 undergraduate students. Participants were required to interpret information delivered by the tactile interface, for two different patients, while they performed a continuous cognitively demanding task. Results indicate that under such conditions, it is possible to deliver critical information with a successful interpretation rate of approximately 85% but not without cost to the continuous demanding task. Future experiments should evaluate more tactile patterns in order to increase their interpretation success rate, and evaluate the use of these tactile cues with clinicians.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".