Bone Mineral Density, Bone Biomarkers, and Joints in Acute, Post, and Long COVID-19: A Systematic Review
Bibliographic record
Abstract
SARS-CoV-2 is highly transmissible and affects the respiratory system. People with COVID-19 are at higher risk of physical and mental health conditions, which could impact bone health. The aim of this review was to explore the effects of COVID-19 on BMD, BTMs, and joints. An electronic search of the PubMed, Web of Science, Scopus, and Ovid Medline databases considered studies published between 1 January 2020 and 1 November 2023. The search was limited to English, original studies in adult humans. The title and abstract of the identified papers were screened, followed by a full-text review using inclusion and exclusion criteria. The data extracted included the study and participant characteristics, BTMs, BMD, and joint abnormalities. The Newcastle-Ottawa scale quality assessment tool was used to assess the risk of bias. Five studies involving 305 out of 495 infected individuals observed a reduced BMD after COVID-19, with the most significant reduction occurring a year later. Both bone resorption and bone formation markers decreased, while regulatory markers showed higher levels in infected patients. COVID-19 may harm bone health by increasing bone regulatory markers and reducing bone formation and absorption, leading to a lower BMD. Elderly, frail, and osteopenic or osteoporotic individuals are at higher risk and should be regularly monitored for bone loss if they have long COVID.
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
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".