Cellular and molecular biomarkers of long COVID: a scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Long-COVID (LC) encompasses diverse symptoms lasting months after the initial SARS-CoV-2 infection. Symptoms can be debilitating and affect the quality of life of individuals with LC and their families. Although the symptoms of LC are well described, the aetiology of LC remains unclear, and consequently, patients may be underdiagnosed. Identification of LC specific biomarkers is therefore paramount for the diagnosis and clinical management of the syndrome. This scoping review describes the molecular and cellular biomarkers that have been identified to date with potential use for diagnosis or prediction of LC. METHODS: This review was conducted using the Joanna Briggs Institute (JBI) Methodology for Scoping Reviews. A search was executed in the MEDLINE and EMBASE databases, as well as in the grey literature for original studies, published until October 5th, 2022, reporting biomarkers identified in participants with LC symptoms (from all ages, ethnicities, and sex), with a previous infection of SARS-CoV-2. Non-English studies, cross-sectional studies, studies without a control group, and pre-prints were excluded. Two reviewers independently evaluated the studies, extracted population data and associated biomarkers. FINDINGS: 23 cohort studies were identified, involving 2163 LC patients [median age 51.8 years, predominantly female sex (61.10%), white (75%), and non-vaccinated (99%)]. A total of 239 candidate biomarkers were identified, consisting mainly of immune cells, immunoglobulins, cytokines, and other plasma proteins. 19 of the 239 candidate biomarkers identified were evaluated by the authors, by means of receiver operating characteristic (ROC) curves. INTERPRETATION: Diverse cellular and molecular biomarkers for LC have been proposed. Validation of candidate biomarkers in independent samples should be prioritized. Modest reported performance (particularly in larger studies) suggests LC may encompass many distinct aetiologies, which should be explored e.g., by stratifying by symptom clusters and/or sex. FUNDING: Dr. Tebbutt has received funding from the Canadian Institutes of Health Research (177747) to conduct this work. The funding source was not involved in this scoping review, or in the decision to submit this manuscript for publication.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it