A Comparison of 2 Assessment Protocols to Specifically Target Abdominal Muscle Endurance
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to compare 2 variations of a test designed to evaluate abdominal muscle endurance. METHODS: This study included 21 healthy adults (10 men and 11 women) aged 23.2 ± 3.3 years. Participants recruited from a chiropractic institution performed 2 fatiguing protocols (with a lordotic posture or free of instructions), each immediately preceded and followed by a maximum voluntary contraction. Force data and surface electromyography of 6 muscles were recorded. The influence of posture on endurance time as well as the effect of posture on MedF/time slopes for each individual muscle throughout the first 4 30-seconds time segments was assessed. RESULTS: Mean time until exhaustion was 261.3 ± 149.8 seconds for the lordotic condition and 358.8 ± 206.4 seconds for the free condition. The lordotic condition induced significantly more fatigue than the free condition in 3 muscles during the first 30 seconds. However, both conditions induced similar levels of fatigue for the following 30 seconds. After the first 60 seconds, no significant differences in fatigability were noted between the 2 experimental conditions. CONCLUSION: For the subjects studied, lumbar lordosis had a significant influence on trunk muscle fatigue during abdominal muscle endurance assessment. Specifically targeting the abdominal muscles during an endurance task remains a challenge.
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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 | 0.003 | 0.007 |
| 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 | 0.000 |
| 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".