Agreement between vertical ground reaction force and ground reaction force vector in five common clinical tests
Bibliographic record
Abstract
Mechanography is an innovative method to evaluate lower-limb dynamic muscle function.This technique is generally performed on force platforms that measure only the vertical component of ground reaction force (GRF).The underlying assumption is that medio-lateral and antero-posterior forces do not contribute significantly to the GRF in jumping and rising tests.The goal of this study was to establish the validity of this assumption.Fifteen healthy adults (mean age [SD]: 30 [11] years; mean height [SD]: 1.68 [0.12] m; mean body mass: 70 [18] kg) performed three repetitions of five different tests in the following order: multiple two-legged hopping, multiple one-legged hopping, single two-legged jump, heel-rise test and chair-rise test.An excellent agreement was found between peak GRF and peak vertical GRF.In each of the five tests, peak vertical GRF represented more than 99% of peak GRF.Moreover, the limits of agreement ranged between 0.05% (multiple two-legged hopping test) and 0.4% (heel-rise test) of the averaged peak force measurements.Therefore measuring only the vertical component of ground reaction force in healthy participants is appropriate for the five tests used in the present study.
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 | 0.007 | 0.037 |
| 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) | 0.002 | 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".