Which common NIRS variable reflects muscle estimated lactate threshold most closely?
Bibliographic record
Abstract
Various near-infrared spectroscopy (NIRS) variables have been used to estimate muscle lactate threshold (LT), but no study has determined which common NIRS variable best reflects muscle estimated LT. Establishing the inflection point of 2 regression lines for deoxyhaemoglobin (DeltaHHb(i.p).), oxyhaemoglobin (DeltaO2Hb(i.p.)), and tissue oxygenation index (TOIi.p.), as well as for blood lactate concentration, we then investigated the relationships between NIRS variables and ventilatory threshold (VT), LT, or maximal tissue hemoglobin index (nTHImax) during incremental cycling exercise. DeltaHHb(i.p.) and TOI(i.p.) could be determined for all 15 subjects, but DeltaO2Hb(i.p.) was determined for only 11 subjects. The mean absolute values for the 2 measurable slopes of the 2 continuous linear regression lines exhibited increased changes in 3 NIRS variables. The workload and VO2 at DeltaO2Hb(i.p.) and nTHImax were greater than those at VT, LT, DeltaHHb(i.p.), and TOI(i.p.). For workload and VO2, DeltaHHb(i.p.) was correlated with VT and LT, whereas DeltaO2Hb(i.p.) was correlated with nTHImax, and TOI(i.p.) with VT and nTHImax. These findings indicate that DeltaO2Hb strongly corresponds with local perfusion, and TOI corresponds with both local perfusion and deoxygenation, but that DeltaHHb can exactly determine deoxygenation changes and reflect O2 metabolic dynamics. The finding of strongest correlations between DeltaHHb and VT or LT indicates that DeltaHHb is the best variable for muscle LT estimation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".