Motion Analysis of Normal Patellar Tendon Reflex
Bibliographic record
Abstract
BACKGROUND: Reflex assessment, an essential element in the investigation of the motor system, is currently assessed through qualitative description, which lacks of normal values in the healthy population. This study quantified the amplitude and latency of patellar tendon reflex in normal subjects using motion analysis to determine the factors affecting the reflex amplitude. METHODS: 100 healthy volunteers were recruited for patellar tendon reflex assessments which were recorded using a motion analysis system. Different levels of input strength were exerted during the experiments. RESULTS: A linear relationship was found between reflex input and reflex amplitude (r = 0.50, P <0.001). The left knee was found to exhibit 26.3% higher reflex amplitude than the right (P <0.001). The Jendrassik manoeuvre significantly increased reflex amplitude by 34.3% (P = 0.001); the effect was especially prominent in subjects with weak reflex response. Reflex latency normality data were established, which showed a gradual reduction with increasing input strength. CONCLUSION: The quantitative normality data and findings showed that the present method has great potential to objectively quantify deep tendon reflexes. Analyse du mouvement du réflexe rotulien normal.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".