Outcome quality of in-patient cardiac rehabilitation in elderly patients – identification of relevant parameters
Bibliographic record
Abstract
BACKGROUND: Outcome quality management requires the consecutive registration of defined variables. The aim was to identify relevant parameters in order to objectively assess the in-patient rehabilitation outcome. METHODS: From February 2009 to June 2010 1253 patients (70.9 ± 7.0 years, 78.1% men) at 12 rehabilitation clinics were enrolled. Items concerning sociodemographic data, the impairment group (surgery, conservative/interventional treatment), cardiovascular risk factors, structural and functional parameters and subjective health were tested in respect of their measurability, sensitivity to change and their propensity to be influenced by rehabilitation. RESULTS: The majority of patients (61.1%) were referred for rehabilitation after cardiac surgery, 38.9% after conservative or interventional treatment for an acute coronary syndrome. Functionally relevant comorbidities were seen in 49.2% (diabetes mellitus, stroke, peripheral artery disease, chronic obstructive lung disease). In three key areas 13 parameters were identified as being sensitive to change and subject to modification by rehabilitation: cardiovascular risk factors (blood pressure, low-density lipoprotein cholesterol, triglycerides), exercise capacity (resting heart rate, maximal exercise capacity, maximal walking distance, heart failure, angina pectoris) and subjective health (IRES-24 (indicators of rehabilitation status): pain, somatic health, psychological well-being and depression as well as anxiety on the Hospital Anxiety and Depression Scale). CONCLUSION: The outcome of in-patient rehabilitation in elderly patients can be comprehensively assessed by the identification of appropriate key areas, that is, cardiovascular risk factors, exercise capacity and subjective health. This may well serve as a benchmark for internal and external quality management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".