The Impact of Test Medium on Use of Visual Analogue Scales
Bibliographic record
Abstract
OBJECTIVES: Visual analog scales are frequently used as a means of allowing participants to rate symptoms during clinical trials. The accuracy and reproducibility of these scales play an important role in determining the experimental value of the data they provide. This study was initiated to compare the data collected using paper- and computer-based (Tablet PC) analog scales to better understand the variability in data provided by a visual analog scale. METHODS: Thirty participants rated ocular comfort, redness, and clarity of vision (right and left eyes) on a nondemarcated horizontal line on both paper and a Tablet PC. Measurements were taken in the morning between the hours of 8:30 and 10:30 am and again the same day between 2:30 and 4:30 pm. RESULTS: The mean difference between the measures recorded in the morning for the 2 media was 2.6 +/- 0.9 (confidence intervals, 2 standard errors of the differences) units on a 100 unit scale, with the Tablet PC having the higher mean measure. The limits of agreement (2 standard deviations of the differences) was 9.4 units. Comparing the difference of the differences (1.0 +/- 1.3) between the 2 methods of measure (morning vs. afternoon) the visual analog scales on the Tablet PC seemed to have good reproducibility of agreement in comparison with the paper version. CONCLUSIONS: Discrepancy analysis yielded no significant difference and slight bias between paper- and computer-based analog scales. Repeatability of measures using the Tablet PC was also demonstrated. These results suggest that the choice of medium does not significantly influence the outcome for subjective analog scales.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.067 | 0.346 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".