Muscle Spindle Traffic in Functionally Unstable Ankles During Ligamentous Stress
Bibliographic record
Abstract
CONTEXT: Ankle sprains are common in athletes, with functional ankle instability (FAI) developing in approximately half of cases. The relationship between laxity and FAI has been inconclusive, suggesting that instability may be caused by insufficient sensorimotor function and dynamic restraint. Research has suggested that deafferentation of peripheral mechanoreceptors potentially causes FAI; however, direct evidence confirming peripheral sensory deficits has been elusive because previous investigators relied upon subjective proprioceptive tests. OBJECTIVE: To develop a method for simultaneously recording peripheral sensory traffic, joint forces, and laxity and to quantify differences between healthy ankles and those with reported instability. DESIGN: Case-control study. SETTING: University laboratory. PATIENTS OR OTHER PARTICIPANTS: A total of 29 participants (age = 20.9 ± 2.2 years, height = 173.1 ± 8.9 cm, mass = 74.5 ± 12.7 kg) stratified as having healthy (HA, n = 19) or unstable ankles (UA, n = 10). INTERVENTION(S): Sensory traffic from muscle spindle afferents in the peroneal nerve was recorded with microneurography while anterior (AP) and inversion (IE) stress was applied to ligamentous structures using an ankle arthrometer under test and sham conditions. MAIN OUTCOME MEASURE(S): Laxity (millimeters or degrees) and amplitude of sensory traffic (percentage) were determined at 0, 30, 60, 90, and 125 N of AP force and at 0, 1, 2, 3, and 4 Nm of IE torque. Two-factor repeated-measures analyses of variance were used to determine differences between groups and conditions. RESULTS: No differences in laxity were observed between groups (P > .05). Afferent traffic increased with increased force and torque in test trials (P < .001). The UA group displayed decreased afferent activity at 30 N of AP force compared with the HA group (HA: 30.2% ± 9.9%, UA: 17.1% ± 16.1%, P < .05). CONCLUSIONS: The amplitude of sensory traffic increased simultaneously with greater ankle motion and loading, providing evidence of the integrated role of capsuloligamentous and musculotendinous mechanoreceptors in maintaining joint sensation. Unstable ankles demonstrated diminished afferent traffic at low levels of force, suggesting the early detection of joint loading may be compromised.
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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 | 0.001 |
| 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".