Bibliographic record
Abstract
The feasibility o f estimating lumbar mechanics in-vivo was evaluated using ultrasound imaging.Images were obtained while subjects were seated, with the pelvis fixed, and pulled on an anchored cable by isometrically contracting trunk muscles at different force levels.Linear regression analysis was used to identify ultrasound measurements which were correlated with trunk force.Results suggest that ultrasound is more suitable for estimating lumbar mechanics during lateral flexion than extension of the trunk.A linear trend was found between changes in thickness o f some muscles and trunk force, which could provide an alternative to invasive intramuscular electrodes for measuring the activity o f non-superficial muscles.A significant limita tion, however, is that the magnitude o f the changes were frequently very close to the ultrasound resolution. s o m m a ir eLa possibilité d 'estimer la mécanique lombaire in-vivo a été évaluée par imagerie ultrasonique.Les images ont été obtenues, alors que le patient était assis, le bassin fixé, et étiré au moyen d'un câble, contractant isométriquement les muscles du tronc à différents niveaux de force.Une régression linéaire a été utilisée pour identifier les mesures d 'ultrasons corrélées avec la force du tronc.Les résultats suggèrent que les ultrasons sont mieux adaptés à l 'estimation de la mécanique lombaire durant une flexion latérale que pendant une ex tension du tronc.Une relation linéaire a été trouvée entre les changements d 'épaisseur de certains muscles et la force du tronc, ce qui pourrait fournir une alternative aux électrodes intramusculaires invasives utilisées pour mesurer l'activité des muscles non-superficiels.Cependant, l 'amplitude des changements, fréquemment très proche de la résolution ultrasonique, représente une limitation significative.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".