Assessing the visual vertical: how many trials are required?
Bibliographic record
Abstract
BACKGROUND: The visual vertical (VV) consists of repeated adjustments of a luminous rod to the earth vertical. How many trials are required to reach consistency in this measure? This question has never been addressed despite the widespread clinical use of the measurement in stroke rehabilitation. METHODS: VV perception was assessed (10 trials) in 117 patients undergoing rehabilitation after a first hemisphere stroke. The intraclass correlation coefficient (ICC) and standard error of measurement (SEM) were calculated for each patient category: with contralesional VV bias (n = 48), ipsilesional VV bias (n = 17) and normal VV (n = 52). RESULTS: For patients with VV biases, 6 trials were required to reach high inter-trial reliability (contralesional: ICC = 0.9, SEM = 1.36°; ipsilesional: ICC = 0.896, SEM = 0.96°). For patients with normal VV, a minimum of 10 trials was required (ICC = .728, SEM = 1.13°). A set of 6 trials correctly classified 96 % of patients. CONCLUSIONS: In the literature, 10 is the most frequently used number of trials used to assess VV orientation. Our study shows that 10 trials are required to adequately measure VV orientation in non-selected subacute stroke patients. For complex protocols imposing a decrease in the number of trials in each condition, 6 trials are needed to identify VV biases in most patients.
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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 | 0.018 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".