Lyme disease associated neurological and musculoskeletal symptoms: A systematic review and meta-analysis
Bibliographic record
Abstract
Background and objective: , presents major health challenges worldwide, leading to serious neurological and musculoskeletal issues that impact patients' lives and healthcare systems. This systematic review and meta-analysis aim to determine the prevalence and link between Lyme disease and these complications, aiming to enhance clinical and public health approaches. Methods: We systematically searched PubMed, EMBASE, and Web of Science up until April 01, 2024, to find studies reporting the prevalence and severity of neurological and musculoskeletal complications associated with Lyme disease. Screening and data extraction were conducted using Nested Knowledge software. Two independent reviewers performed the quality assessment using the Newcastle-Ottawa Scale. Meta-analyses were performed using R software v4.3, employing a random-effects model. Results: Out of 3576 records, 17 studies were included, involving 3932 participants. These studies revealed significant prevalence of musculoskeletal symptoms (21.1%) and neurological disabilities (18%) among Lyme disease patients. The analysis showed a notable increase in risk for both complications in individuals with Lyme disease, with pooled Risk Ratios (RR) of 1.82 for musculoskeletal symptoms and 1.64 for neurological disabilities, indicating a significantly higher risk compared to control groups. Although heterogeneity across the studies was high, sensitivity analysis confirmed the consistency of our findings. Additionally, there was evidence of publication bias. Conclusion: The study reveals significant neurological and musculoskeletal complications in Lyme disease patients, emphasizing the importance of early diagnosis, comprehensive treatment, and supportive care. The noted heterogeneity and potential publication bias highlight the need for transparent research and further study on long-term outcomes.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.044 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".