Interventions for the management of post COVID-19 condition (long COVID): Protocol for a living systematic review & network meta-analysis
Bibliographic record
Abstract
Abstract Background Up to 15% of survivors of COVID-19 infection experience long-term health effects, including fatigue, myalgia, and impaired cognitive function, termed post COVID-19 condition or long COVID. Several trials that study the benefits and harms of various interventions to manage long COVID have been published and hundreds more are planned or are ongoing. Trustworthy systematic reviews that clarify the benefits and harms of interventions are critical to promote evidence-based practice. Objective To create and maintain a living systematic review and network meta-analysis addressing the benefits and harms of pharmacologic and non-pharmacologic interventions for the treatment and management of long COVID. Methods Eligible trials will randomize adults with long COVID, to pharmacologic or non-pharmacologic interventions, placebo, sham, or usual care. We will identify eligible studies by searches of MEDLINE, EMBASE, CINAHL, PsycInfo, AMED, and CENTRAL, from inception, without language restrictions. Reviewers will work independently and in duplicate to screen search records, collect data from eligible trials, including trial and patient characteristics and outcomes of interest, and assess risk of bias. Our outcomes of interest will include fatigue, pain, post-exertional malaise, changes in education or employment status, cognitive function, mental health, dyspnea, quality of life, patient-reported physical function, recovery, and serious adverse events. For each outcome, when possible, we will perform a frequentist random-effects network meta-analysis. When there are compelling reasons to suspect that certain interventions are only applicable or effective for a subtype of long COVID, we will perform separate network meta-analyses. The GRADE approach will guide our assessment of the certainty of evidence. We will update our living review biannually, upon the publication of a seminal trial, or when new evidence emerges that may change clinical practice. Conclusion This living systematic review and network meta-analysis will provide comprehensive, trustworthy, and up-to-date summaries of the evidence addressing the benefits and harms of interventions for the treatment and management of long COVID. We will make our findings available publicly and work with guideline producing organizations to inform their recommendations.
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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 | 0.067 | 0.106 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.020 | 0.025 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.085 | 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".