Precision based guidelines for sub-maximal normalisation task selection for trunk extensor EMG
Bibliographic record
Abstract
AIM: The object of this study was to quantify the contribution of sub-maximal normalisation to the overall variance of exposure parameters describing erector spinae (ES) activity, and to provide guidelines for task selection which minimize methodological variance. METHODS: ES EMG was measured from three locations (T9, L1 and L5 levels) on fifteen men performing a manual materials handling task in the laboratory on three separate days. Four repeats of each of eleven sub-maximal normalisation tasks (eight static, three dynamic) were collected, work data were normalised to each task and repeat, and exposure parameters calculated. The unique contribution of normalisation to the overall variance was determined for each task and exposure parameter using variance component analyses. Normalisation tasks were scored according to their relative contributions to the overall variance and coefficients of variation. RESULTS: A prone task, similar to the Biering-Sørensen test posture, was the most repeatable for all electrode locations and across all exposure parameters. Thoracic level normalisation typically showed poorer repeatability than lumbar normalisation. DISCUSSION: To maximize measurement precision, we recommend that future ES EMG studies employing sub-maximal normalisation utilise said prone task. An alternate normalisation task specific to thoracic level ES muscles may be warranted.
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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 | 0.041 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".