Abnormal blood lactate accumulation during repeated exercise testing in myalgic encephalomyelitis/chronic fatigue syndrome
Bibliographic record
Abstract
Post-exertional malaise and delayed recovery are hallmark symptoms of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Studies on repeated cardiopulmonary exercise testing (CPET) show that previous exercise negatively affects oxygen uptake (VO2) and power output (PO) in ME/CFS. Whether this affects arterial lactate concentrations ([Laa]) is unknown. We studied 18 female patients (18–50 years) fulfilling the Canadian Consensus Criteria for ME/CFS and 15 healthy females (18–50 years) who underwent repeated CPETs 24 h apart (CPET1 and CPET2) with [Laa] measured every 30th second. VO2 at peak exercise (VO2peak) was lower in patients than in controls on CPET1 (P < 0.001) and decreased in patients on CPET2 (P < 0.001). However, the difference in VO2peak between CPETs did not differ significantly between groups. [Laa] per PO was higher in patients during both CPETs (Pinteraction < 0.001), but increased in patients and decreased in controls from CPET1 to CPET2 (Pinteraction < 0.001). Patients had lower VO2 (P = 0.02) and PO (P = 0.002) at the gas exchange threshold (GET, the point where CO2 production increases relative to VO2), but relative intensity (%VO2peak) and [Laa] at GET did not differ significantly from controls on CPET1. Patients had a reduction in VO2 (P = 0.02) and PO (P = 0.01) at GET on CPET2, but no significant differences in %VO2peak and [Laa] at GET between CPETs. Controls had no significant differences in VO2, PO or %VO2peak at GET between CPETs, but [Laa] at GET was reduced on CPET2 (P = 0.008). In conclusion, previous exercise deteriorates physical performance and increases [Laa] during exercise in patients with ME/CFS while it lowers [Laa] in healthy subjects.
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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".