Tissue oxygenation during exercise measured with NIRS: a quality control study
Bibliographic record
Abstract
We assess the data quality of calculated tissue oxygen saturation (SO2) and haemoglobin concentrations recorded on muscle during an incremental cycling protocol in healthy volunteers. The protocol was repeated three times at the same day and a fourth time at a different day to estimate the reproducibility of the method. A novel broad-band, spatially resolved spectrometer (SRS) system was employed which allowed us to compare SRS-based oxygenation parameters with modified Lambert-Beer (MLB) data. We found that the inter-subject variation in SO2 (standard deviation about 6 %) is considerably larger than the reproducibility (about 1.5 %) both for same day and different day tests. When changes in SO2 during the cycling test were considered the reproducibility is better than 1 %. Time courses of SRS-based haemoglobin parameters are different from MLB-data with higher reproducibility for SRS. The magnitudes of the haemoglobin changes were found to be considerably larger for the SRS method. Furthermore, the broad band approach was tested against a four-wavelength analysis with the differences found to be negligible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".