Reliability of the two-point method applied in field conditions and its validity in estimating the one-repetition maximum using the load–velocity relationship of the free-weight back squat
Bibliographic record
Abstract
In most studies examining the reliability and validity of the load–velocity relationship (LVR) determined with the two-point method, a pair of points derived from a previously applied protocol involving multiple loads is selected to compute the relationship (multipoint method–MP). While testing only two loads (two-point applied in field conditions–2P FC ) allows for a reliable free-weight back squat LVR determination, it is not known whether the average optimal minimum velocity threshold enables accurate one-repetition maximum (1RM) estimations. LVRs based on the 2P FC were compared to those obtained with the MP, in 18 participants. Reliability of LD0 (load at zero velocity), slope, V0 (velocity at zero load), and Aline (area under the line) determined with the 2P FC was assessed with intraclass correlation coefficients (ICCs) and coefficients of variation (CVs). Absolute percent errors of estimation were compared between MP and 2P FC. Agreement between actual and predicted 1RM was assessed with Bland–Altman plots. LVR parameters were similar between profiling methods. The 2P FC showed acceptable reliability (CVs < 10% and ICCs > 0.70). The absolute percent error of estimation was lower with the 2P FC (6.7% and 4.6%, for MP and . 2P FC respectively). Coaches can determine the LVR of their athletes and further estimate their 1RM relying on the average optimal MVT (with small error < 5%). This can be done by simply measuring mean concentric velocity in response to a practical protocol of two loads. However, caution is advised, as this method may misestimate the 1RM by 14 kg in some individual cases.
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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 | 0.036 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".