Measuring True Change in Individual Patients: Reliable Change Indices of Cardiac Rehabilitation Outcomes, and Implications for Quality Indicators
Bibliographic record
Abstract
Background Mediated by outcomes such as improved exercise capacity, cardiac rehabilitation (CR) reduces morbidity and mortality. For accuracy, an individual CR patient's change must be measured reliably, an issue not typically considered in practice. Drawing from psychometric theory, we calculated reliable change indices (RCIs), to measure individual CR patients' true clinical change, apart from that from error and test practice/exposure, in exercise capacity, anxiety, and depression. Methods Indirectly calculated exercise capacity (peak metabolic equivalents [METs]) and psychological symptoms were each measured twice, 1 week apart, by administering treadmill tests or the Hospital Anxiety and Depression Scale (HADS) to separate samples of 35 (mean age: 59.0 years; 6 women) and 96 (mean age: 64.4 years; 32 women) CR patients, respectively. Using test-retest reliability and mean difference scores from these samples to estimate error and practice/exposure effects, we calculated RCIs for a separate cohort (n = 2066; mean age: 62.0 years; 533 women) who completed 6-month CR, and compared change distributions (worsened/unchanged/improved) based on critical RCIs, mean and percent changes, cut-off scores, and standard deviations. Results Practice/exposure effects were nonsignificant, except the mean HADS anxiety score decreased significantly ( P ≤ 0.013; d = 0.17, small effect). Test-retest reliabilities were high (METs r = 0.934; HADS anxiety score r = 0.912; HADS depression score r = 0.90; P < 0.001). Among 2066 CR patients, RCI distributions differed ( P < 0.001) from those of most other change criteria. Conclusions Change ascertainment depends on criterion choice. A Canadian Cardiovascular Society CR quality indicator of increase by 0.5 MET may be too small to assess individuals' functional capacity change. RCIs offer a pragmatic approach to benchmarking reliable change frequency, and pending further validation, could be used for feedback to individual patients.
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 | 0.044 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".