Clinical tools to quantify torso flexion endurance: Normative data from student and firefighter populations
Bibliographic record
Abstract
Given that torso muscle endurance is one of the few metrics that has been shown to be linked to having a history of back disorders together with predicting future back disorders, endurance tests for workers have been developed. While some data exists on the V-sit exercise for flexor endurance, some have specifically adopted the plank test. The primary objective of this study was to assess links between the two tests. Two data sets were collected. The first set was obtained from a convenience group of fire fighters where the plank endurance test scores, together with the Biering Sorensen test for extension endurance, were obtained over three years. The second data set was obtained from a tightly controlled cross-sectional study of university students that included scores for both flexor and extensor endurance. 620 fire fighters for the first data set and 181 university students for the second. While flexor endurance in the firefighters peaked when aged in their 40's, extensor endurance peaked in their 20's. In the study of university students, the plank scores were relatively higher than the V-sit scores in males but relatively lower than the V-sit in the females. A pearson correlation test between the paired plank and V-sit scores of each subject rendered a coefficient of r =0.34. This means that the performance on one flexor test only predicted 11% of the score in the other. ANOVA comparison of scores based on their history of having had shoulder or back troubles showed no significant link between V-sit or plank scores. The plank scores are not well correlated with the V-sit scores suggesting that the two measure different variables. Since more data exists for the V-sit, measurement of flexor endurance in occupational settings using this test probably forms a stronger link to back injury.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".