Rehabilitation and COVID-19: rapid living systematic review by Cochrane Rehabilitation Field - third edition. Update as of June 30th, 2021
Bibliographic record
Abstract
INTRODUCTION: This paper updates and summarizes the current evidence informing rehabilitation of patients with COVID-19 and/or describing the consequences of the disease and its treatment. EVIDENCE ACQUISITION: , 2021 were selected, excluding descriptive studies and expert opinions. Papers were categorized according to study design, research question, COVID-19 phase, limitations of functioning of rehabilitation interest, and type of rehabilitation service involved. From this edition, we improved the quality assessment using the Joanna Briggs Institute checklists for observational studies and the Cochrane Risk of Bias Tool for randomized-controlled clinical trials (RCTs). EVIDENCE SYNTHESIS: Twenty-five, out of 3699 papers, were included. They were three RCTs, 13 cross-sectional studies and nine cohort studies. Twenty studies reported data on symptom prevalence (N.=13) or disease natural history (N.=7); and five studies reported intervention effectiveness at the individual level. All study participants were COVID survivors and 48% of studies collected information on participants 6 months or longer after COVID-19 onset. The most frequent risks of bias for RCTs concerned weaknesses in allocation concealment, blinding of therapists, and lack of intention-to-treat analysis. Most analytical studies failed to identify or deal with confounders, describe or deal with dropouts or eventually perform an appropriate statistical analysis. CONCLUSIONS: Most studies in this updated review targeted the prevalence of limitations of functioning of rehabilitation interest in COVID-19 survivors. This is similar to past review findings; however, data in the new studies was collected at longer follow-up periods (up to one year after symptom onset) and in larger samples of participants. More RCTs and analytical observational studies are available, but the methodological quality of recently published studies is low. There is a need for good quality intervention efficacy and effectiveness studies to complement the rapidly expanding evidence from observational studies.
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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 | 0.077 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.008 |
| Bibliometrics | 0.041 | 0.031 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.010 |
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".