Systematizing Inpatient Referral to Cardiac Rehabilitation 2010
Bibliographic record
Abstract
Despite recommendations in clinical practice guidelines, evidence suggests cardiac rehabilitation (CR) referral and use following indicated cardiac events is low. Referral strategies such as systematic referral have been advocated to improve CR use. The objective of this policy position is to synthesize evidence and make recommendations on strategies to increase patient enrollment in CR. A systematic review of 6 databases from inception to January 2009 was conducted. Only primary, published, English-language studies were included. A meta-analysis was undertaken to synthesize the enrollment rates by referral strategy. In all, 14 studies met inclusion criteria. Referral strategies were categorized as systematic on the basis of use of systematic discharge order sets, as liaison on the basis of discussions with allied health care providers, or as other on the basis of patient letters. Overall, there were 7 positive studies, 5 without comparison groups, and 2 studies that reported null findings. The combined effect sizes of the meta-analysis were as follows: 73% (95% CI, 39%-92%) for the patient letters ("other"), 66% (95% CI, 54%-77%) for the combined systematic and liaison strategy, 45% (95% CI, 33%-57%) for the systematic strategy alone, and 44% (95% CI, 35%-53%) for the liaison strategy alone. In conclusion, the results suggest that innovative referral strategies increase CR use. Although patient letters look promising, evidence for this strategy is sparse and inconsistent at present. Therefore we suggest that inpatient units adopt systematic referral strategies, including a discussion at the bedside, for eligible patient groups in order to increase CR enrollment and participation. This approach should be considered best practice for further investigation.
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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 | 0.022 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".