Evaluating the evidence for macrophage presence in skeletal muscle and its relation to insulin resistance in obese mice and humans: a systematic review protocol
Bibliographic record
Abstract
OBJECTIVES: The current global rates of obesity and type 2 diabetes are staggering. In order to implement effective management strategies, it is imperative to understand the mechanisms of obesity-induced insulin resistance and diabetes. Macrophage infiltration and inflammation of the adipose tissue in obesity is a well-established paradigm, yet the role of macrophages in muscle inflammation, insulin resistance and diabetes is not adequately studied. In this systematic review, we will examine the evidence for the presence of macrophages in skeletal muscle of obese humans and mice, and will assess the association between muscle macrophages and insulin resistance. We will identify published studies that address muscle macrophage content and phenotype, and its association with insulin resistance. We will search MEDLINE/PubMed, EMBASE, and Web of Science for eligible studies. Grey literature will be searched in ProQuest. Quality assessment will be conducted using the Systematic Review Centre for Laboratory Animal Experimentation risk of bias Tool for animal studies. RESULTS: The findings of this systematic review will shed light on immune-metabolic crosstalk in obesity, and allow the consideration of targeted therapies to modulate muscle macrophages in the treatment and prevention of diabetes. The review will be published in a peer-reviewed journal and presented at conferences.
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 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 | 0.047 | 0.074 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.021 | 0.016 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".