Non-pharmacological interventions to support coronary artery bypass graft (CABG) patient recovery following discharge: protocol for a scoping review
Bibliographic record
Abstract
BACKGROUND: In Canada, approximately 15 000 people undergo coronary artery bypass grafting (CABG) each year. However, 9.5% of these patients are urgently readmitted to hospital within 30 days of surgery. Postoperative interventions following discharge play an important role in reducing readmissions and improving CABG patient outcomes. Therefore, it is important to determine effective interventions available to enhance CABG patient recovery following postoperative discharge. OBJECTIVES: Our scoping review aims to identify non-pharmacological interventions available to support recovery of patients who are discharged after CABG in the community setting. METHODS: The methodological framework described by Arksey and O'Malley will be applied to this review. Our search strategy will include electronic databases (Medline, Embase, Cochrane Library and CINAHL), and studies will be screened and reviewed by two independent reviewers. Studies looking at non-pharmacological interventions targeting patients who are discharged after CABG will be included. Preliminary searches were conducted March 2022 and following abstract screening, full-text screening was completed May 2023. Data extraction is planned to begin September 2023 with an expected finish date of October 2023. The study is expected to be completed by January 2024. ETHICS AND DISSEMINATION: This scoping review will retrieve and analyse previously published studies in which informed consent was obtained by primary investigators. Therefore, no ethical review or approval will be required. This scoping review aims to enumerate available non-pharmacological interventions to support recovery of patients who are discharged after CABG and identify gaps in postoperative recovery after discharge to support the development of innovative and targeted interventions. On completion of this review, we will ensure broad dissemination of our findings through peer-reviewed, open-access journals, conference presentations and hold meetings to engage stakeholders, including clinicians, policy makers and others.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
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 | 0.076 | 0.078 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.064 | 0.011 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".