Visualising Magnitude:Graphical Number Representations Help Users Detect Large Number Entry Errors
Bibliographic record
Abstract
Nurses frequently have to program infusion pumps to deliver a prescribed quantity of drug over time. Occasional errors are made in the performance of this routine number entry task, resulting in patients receiving the incorrect dose of a drug. While many of these number entry errors are inconsequential, others<br/>are not; infusing 100 ml of a drug instead of 10 ml can be fatal. This paper investigates whether a supplementary graphical number representation, depicting the magnitude of a number, can help people detect number entry errors. An experiment was conducted in which 48 participants had to enter numbers from a ‘prescription sheet’ to a computer interface using a keyboard. The graphical representation was supplementary and was shown both on the ‘prescription sheet’ and the device interface. Results show that while overall more errors were made when the graphical representation was visible, the graphical representation helped participants to detect larger number entry errors (i.e., those that were out by at least an order of magnitude). This work suggests that a graphical number entry system that visualizes magnitude of number can help people detect serious number entry errors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".