Noninvasive estimation of microvascular O<sub>2</sub> provision during exercise on-transients in healthy young males
Bibliographic record
Abstract
Two methods for estimating changes in microvascular O2 delivery during the on-transient of exercise were evaluated. They were tested to assess the role of the adjustment of the estimated microvascular O2 delivery in the speeding of Vo2 kinetics during a Mod1-Hvy-Mod2 protocol (Mod, moderate-intensity exercise; Hvy, heavy-intensity "priming" exercise), in which Mod2 is preceded by a bout of Hvy. Mod pulmonary Vo2 (Vo(2p)) and deoxy-hemoglobin [HHb] data were collected in 12 males (23 ± 3 yr); response profiles were fit with a monoexponential. Signals were also 1) scaled to a relative % of the response (0-100%) to calculate the [HHb]/Vo2 ratio for each individual and 2) rearranged in the Fick equation for estimation of capillary blood flow (Q(cap)). A transient [HHb]/Vo2 "overshoot" observed in Mod1 (1.06 ± 0.05; P < 0.05) was absent during Mod2 (1.01 ± 0.06; P > 0.05); reductions in the [HHb]/Vo2 ratio (Mod1 - Mod2) were related to reductions in phase II τVo(2p) (r = 0.82; P < 0.05). For Q(cap), a near-exponential response was observed in 8/12 subjects in Mod1 and only in 4/12 subjects in Mod2. The Q(cap) profile was shown to be highly dependent on the [HHb] baseline-to-amplitude ratio. Thus, accurate and physiologically consistent estimations of Q(cap) were not possible in most cases. This study confirmed that priming exercise results in an improved O2 delivery as shown by the decreased [HHb]/Vo2) ratio that was related to the smaller τVo2 in Mod2. Additionally, this study suggested that Q(cap) analysis may not be valid and should be interpreted with caution when assessing microvascular delivery of O2.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".