Evaluating Arterial Blood Flow Limitation Using Muscle Oxygenation and Cycling Power
Bibliographic record
Abstract
OBJECTIVE: To explore the combination of measuring muscle oxygenation with near-infrared spectroscopy (NIRS) and cycling power during provocative incremental exercise for the detection of iliac arterial blood flow limitation (IAFL) in an otherwise healthy, well-trained cyclist. DESIGN: Case report and methodological pilot study. SETTING: University research setting. PATIENT: A well-trained amateur competitive male cyclist, aged 31 years, presenting with symptoms consistent with IAFL, but in whom diagnostic imaging was equivocal. INTERVENTIONS: Four ramp incremental cycling tests performed on separate days to exercise intolerance, in a randomized order, in either typical race position (RP) or modified upright position (UP). MAIN OUTCOME: A novel ratio of unilateral cycling power to NIRS-derived muscle oxygenation termed "power-deoxygenation factor" was measured during provocative incremental exercise and compared with other NIRS-derived measures of vascular responsiveness and performance outcomes across the 2 body position conditions. RESULTS: The power-deoxygenation factor was able to show clinically important, progressive differences between the affected and unaffected limbs, coinciding with worsening performance impairments related to the body position that were not detected with traditional measures of vascular responsiveness taken after exercise. CONCLUSIONS: This method was used to detect bilateral differences consistent with IAFL in a cyclist where traditional diagnostic criteria were equivocal, but subsequent intraoperative findings confirmed the diagnosis. A similar screening test could be performed noninvasively and without requiring specialized medical care. Future work should investigate the validity and sensitivity of this methodology to improve the ability to identify and monitor athletes with IAFL.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".