'Reverse Lactate Threshold‘ - A Novel Approach To High-resolution, Single-session Determination Of The Anaerobic-threshold
Bibliographic record
Abstract
Existing single-session tests for estimating of the Anaerobic-Threshold (AnT) or Maximal Lactate Steady-State (MLSS) show unsatisfactory validity, reliability, or resolution. The main shortcomings of the accepted 'Gold Standard‘, the multi-session MLSS test, are prohibitive impracticality and unproven reliability. PURPOSE: To introduce the proposed single-session 'Reverse Lactate Threshold‘ test (RLT) for AnT/MLSS determination, validate it against the accepted 'Gold Standard‘ (MLSS test), and test its reliability. METHODS: The RLT consists of 2 contiguous series of continuous 4-min stages. A 3-5-stage series is progressively incremented from a mild work load to 5-20% above the presumed MLSS intensity. It is followed by a 3-6-load reverse series decremented in small steps. Capillary blood is sampled at the end of each stage for lactate concentration ([La]). [La] is then plotted against load and the power output at peak [La] during the reverse series is taken as the RLT-determined AnT. Four athletes of different training levels and disciplines (rowing, cycling, running) served for RLT validation and completed all RLT and MLSS-verification tests. Accepted MLSS-determination criterion was used for verification ([La] rise of ≤1.0mM in the last 20 min of the 30-min MLSS test). One athlete was retested, following a 2.5-month endurance-training period, to gauge RLT‘s sensitivity to fitness changes. Additional 10 trained and untrained cyclists were tested twice, 2-5 days apart, to evaluate the RLT‘s test-retest reliability. RESULTS: RLT-MLSS agreement within 1W (<0.5%) or <0.1 mph (∼0.5%) was shown in all 4 'validation‘ subjects. Post-training, both RLT-determined AnT and MLSS increased by 15W and were in complete agreement. The test-retest coefficient in the reliability testing was 0.998. CONCLUSIONS: The RLT precisely predicted MLSS intensity in all subjects. Its single-session attribute makes it highly practical for routine testing of trained and untrained individuals. Based on the present data, the RLT appears to at least match the MLSS test‘s accuracy and exceed its resolution in estimating true AnT exercise intensity and in reflecting training-induced AnT changes. The observed level of reliability is higher than any reported for any existing test.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".