Reliability and validity of the lactate-minimum test. A revisit.
Bibliographic record
Abstract
AIM: The Lactate-Minimum Test (LMT) is a high-resolution, physiologically elegant test for estimating the anaerobic threshold (AnT), or the Maximal Lactate Steady-State (MLSS). Nevertheless, it has not gained the acceptance level of typical progressive lactate-response tests (PLRT). Aim of this study was to compare LMT's validity and reviewer reliability vs. a PLRT-type test and re-evaluate the justification for LMT's dismissal. METHODS: Sixteen male distance trained runners (37.1±11.6 yrs) were included in the study. MLSS, LMT, and PLRT tests were performed in separate sessions. Two reviewers, blind to the subjects' identity, independently determined LMT and PLRT's threshold velocities (VLMT, VPLRT) twice. Additionally, VLMT was determined objectively, using best-fit polynomial regressions (VLMTP). RESULTS: VPLRT, VLMT and VLMTP correlated well with VMLSS (r=0.92, 0.90, 0.93, resp.). VPLRT was identical to VMLSS (13.54 km·h-1), but VLMT and VLMTP were 0.33 and 0.46 km·h-1 lower, respectively. Inter-reviewer reliability was higher for VLMT than VPLRT (ICC=0.96 vs. 0.57, resp.). Intra-reviewer reliability showed a similar pattern. CONCLUSION: LMT's underestimation of MLSS appears corrigible. The validity of corrected LMT appears comparable to that of PLRT, while its reliability, objectivity and resolution are superior. Although neither test is a perfect MLSS-substitute, the corrected LMT is not inferior to PLRT-type testing and cannot be dismissed.
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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 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".