2019 Vestibular Oriented Research Meeting, May 19-22, 2019, Hosted by The Ohio State University Wexner Medical Center at the Marriott at the University of Dayton
Bibliographic record
Abstract
Both vestibular function and balance degrade with age, and balance degradation contributes to falls.While multiple causes contribute to both balance declines and falls, there have been few comprehensive empirical investigations focused on the effect of aging on vestibular function and on the specifi c vestibular contributions to balance that mediate (i.e., explain a signifi cant fraction of) the effect of age on balance.Our goals were to quantify age-effects on vestibular function, to determine if vestibular function signifi cantly mediates the effect of age on balance, and to quantify the fraction of any such statistically signifi cant age-effect on balance.Balance was quantifi ed as complete/incomplete on a standard Romberg 4-condition foam balance test for 99 subjects.Vestibular thresholds for 5 motions (0.2 Hz roll tilt and 1 Hz roll tilt, yaw rotation, y-translation, and z-translation) were determined using standard methods with motion provided by a Moog 6DOF
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.242 | 0.053 |
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".