Effects of home healthcare for adults with chronic respiratory diseases and post-COVID-19 syndrome on hospital bed turnover rate: a protocol of systematic review with meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Chronic respiratory diseases (CRDs) have a high prevalence, morbidity and mortality worldwide. After the COVID-19 pandemic, the number of patients readmitted after hospital discharge increased. For some populations, early hospital discharge and home healthcare may reduce health costs in patients treated at home when compared with those hospitalised. This study aims to systematically review the effectiveness of home healthcare for patients with CRDs and post-COVID-19 syndrome. METHODS AND ANALYSIS: We will search on MEDLINE, CENTRAL, Embase and PsycINFO. We will include randomised controlled trials (RCTs) and non-RCT studies reported in full text and abstracts. No language restriction will be applied. We will include studies related to adults with a diagnosis of CRDs or post-COVID-19 syndrome that compared in-patient hospital care with any home healthcare. We will exclude studies with participants with neurological, mental diseases, cancer or pregnant women. Two review authors will screen abstracts and select the eligible studies. To investigate the risk of bias, we will use the Cochrane 'Risk of Bias' tool for RCT, and the Risk of Bias In Non-randomised Studies-of Interventions for non-RCT. We will use the five Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) considerations to assess the quality of the evidence. Patients and the public will be involved in the preparation, execution and implementation phases of the review. ETHICS AND DISSEMINATION: No ethical approval is required because only published data will be analysed. The publication of the results in peer-reviewed journals and at relevant conferences will guide the direction of future research in the field and healthcare practice. The results will also be disseminated in plain language on social media to disseminate the knowledge to society and the public interested in the topic.
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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 | 0.089 | 0.120 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.027 | 0.044 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.003 |
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".