Mitigating the bilateral deficit: reducing neural deficits through residual force enhancement and activation reduction
Bibliographic record
Abstract
PURPOSE: The bilateral deficit (BLD) is characterized by a reduction in maximal voluntary torque during a bilateral contraction relative to the sum of left and right unilateral contractions. The BLD has been attributed to interhemispheric inhibition as a result of unilateral torque differences between limbs. If the BLD is the result of interhemispheric inhibition, lowering activation for a torque matching task, as shown in residual force enhancement (RFE), may help overcome the decrease in neural drive during bilateral contractions. Therefore, the purpose of the present study was to determine whether RFE could reduce the BLD. METHODS: Participants (n = 12) performed both isometric and RFE MVCs of the elbow flexors under three conditions: (1) unilateral-left; (2) unilateral-right; and (3) bilateral. To directly address the purpose of the study, a sub-group of participants that displayed both RFE and a BLD ("Responders", n = 6) were selected from the participant pool. RESULTS: "Responders" displayed RFE (7.1 ± 5.3%) and an isometric BLD (BI: - 9.9 ± 3.2%). In the RFE state, the BLD was no longer significant (- 5.8 ± 7.9%), accompanied by the elimination of differences in biceps brachii EMG between arms (left: - 11.7 ± 10.3%; right: - 11.5 ± 13.2%), as seen during isometric contractions (left: - 12.0 ± 23.2%; right: - 21.1 ± 16.6%). CONCLUSION: Residual force enhancement appears to mitigate the BLD, alleviating the effects of a decrease in neural drive by allowing more force for a given level of muscle activation when compared to a purely isometric contraction.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".