Theoretical Repeatability Coefficient of a 100 mm Visual Analog Scale in Children
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine the theoretical repeatability coefficient of a 100 mm visual analog scale (VAS) in children in different circumstances. METHODS: A prospective cohort study was conducted using a convenience sample of patients aged 8 to 17 years presenting to a pediatric emergency department. Patients were asked how they liked a variety of foods (surrogate for stable pain stimulus) on a 100 mm VAS with 4 different sets of questions repeated 3 times: set 1--questions at 3-minute intervals with no specific instructions other than how to complete the VAS; set 2--same format as set 1 except for the duration of the interval (1 min); set 3--same as set 1 except patients were asked to remember their answers; set 4--same as set 1 except patients were shown their previous answers. For each, the repeatability coefficient of the VAS was determined. RESULTS: A total of 100 patients aged 12.1±2.4 years were enrolled. The repeatability coefficient for the questions asked at the 3-minute interval was 12 mm, whereas it was 8 mm when asked at the 1-minute interval. When asked to remember their previous answers or to reproduce them, the repeatability coefficients for the questions were 7 and 6 mm, respectively. DISCUSSION: The conditions of the assessments influence the repeatability coefficient of the VAS. Depending on different circumstances, the repeatability coefficient in children aged 8 to 17 years varies from 6 to 12 mm on a 100 mm VAS.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".