Inter- and intra-observer reliability and agreement of O2Pulse inflection during cardiopulmonary exercise testing: A comparison of subjective and novel objective methodology
Bibliographic record
Abstract
Cardiopulmonary exercise testing (CPET) is the 'gold standard' method for evaluating functional capacity, with oxygen pulse (O2Pulse) inflections serving as a potential indicator of myocardial ischaemia. However, the reliability and agreement of identifying these inflections have not been thoroughly investigated. This study aimed to assess the inter- and intra-observer reliability and agreement of a subjective quantification method for identifying O2Pulse inflections during CPET, and to propose a more robust and objective novel algorithm as an alternative methodology. A retrospective analysis was conducted using baseline data from the HIIT or MISS UK trial. The O2Pulse curves were visually inspected by two independent examiners, and compared against an objective algorithm. Fleiss' Kappa was used to determine the reliability of agreement between the three groups of observations. The results showed almost perfect agreement between the algorithm and both examiners, with a Fleiss' Kappa statistic of 0.89. The algorithm also demonstrated excellent inter-rater reliability (ICC) when compared to both examiners (0.92-0.98). However, a significant level (P ≤0.05) of systematic bias was observed in Bland-Altman analysis for comparisons involving the novice examiner. In conclusion, this study provides evidence for the reliability of both subjective and novel objective methods for identifying inflections in O2Pulse during CPET. These findings suggest that further research into the clinical significance of O2Pulse inflections is warranted, and that the adoption of a novel objective means of quantification may be preferable to ensure equality of outcome for patients.
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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 | 0.038 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".