Maximal strength measurement: A critical evaluation of common methods—a narrative review
Bibliographic record
Abstract
Measuring maximal strength (MSt) is a very common performance diagnoses, especially in elite and competitive sports. The most popular procedure in test batteries is to test the one repetition maximum (1RM). Since testing maximum dynamic strength is very time consuming, it often suggested to use isometric testing conditions instead. This suggestion is based on the assumption that the high Pearson correlation coefficients of r ≥ 0.7 between isometric and dynamic conditions indicate that both tests would provide similar measures of MSt. However, calculating r provides information about the relationship between two parameters, but does not provide any statement about the agreement or concordance of two testing procedures. Hence, to assess replaceability, the concordance correlation coefficient (ρc) and the Bland-Altman analysis including the mean absolute error (MAE) and the mean absolute percentage error (MAPE) seem to be more appropriate. Therefore, an exemplary model based on r = 0.55 showed ρc = 0.53, A MAE of 413.58 N and a MAPE = 23.6% with a range of −1,000–800 N within 95% Confidence interval (95%CI), while r = 0.7 and 0.92 showed ρc = 0.68 with a MAE = 304.51N/MAPE = 17.4% with a range of −750 N–600 N within a 95% CI and ρc = 0.9 with a MAE = 139.99/MAPE = 7.1% with a range of −200–450 N within a 95% CI, respectively. This model illustrates the limited validity of correlation coefficients to evaluate the replaceability of two testing procedures. Interpretation and classification of ρc, MAE and MAPE seem to depend on expected changes of the measured parameter. A MAPE of about 17% between two testing procedures can be assumed to be intolerably high.
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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 | 0.038 | 0.178 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".