Inter-day test–retest reliability and feasibility of isokinetic, isometric, and isotonic measurements to assess quadriceps endurance in people with chronic obstructive pulmonary disease: A multicenter study
Bibliographic record
Abstract
The aims were to determine reliability and feasibility of measurements to assess quadriceps endurance in people with chronic obstructive pulmonary disease. Sixty participants (forced expiratory volume in one second (mean ± standard deviation) 55 ± 18% of predicted, age 67 ± 8 years) were tested in an inter-day, test-retest design. Isokinetic, isometric, and isotonic protocols were performed using a computerized dynamometer. Test-retest relative and absolute reliability was determined via intraclass correlation coefficient (ICC), coefficient of variation (CV%), and limits of agreement (LoA%). Isokinetic total work demonstrated very high relative reliability (ICC: [95% confidence interval] = 0.98 [0.94-0.99]) and the best absolute reliability (CV% (LoA%) = 6.5% (18.0%)). Isokinetic fatigue index, isometric, and isotonic measures demonstrated low-to-high relative reliability (ICC = 0.64 [0.46-0.77], 0.88 [0.76-0.94], 0.91 [0.85-0.94]), and measures of absolute reliability (CV% (LoA%)) were 20.3% (56.4%), 14.9% (40.8%), and 15.8% (43.1%). For isokinetic total work and isometric measurements, participants performed better on retest (4.8% and 10.0%, respectively). The feasibility was similar across protocols with an average time consumption of less than 7.5 minutes. In conclusion, isokinetic, isometric, and isotonic measurements of quadriceps endurance were feasible to a similar extent and presented low-to-very high relative reliability. Absolute reliability seems to favor isokinetic total work measurements.
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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".