Bibliographic record
Abstract
Intervertebral disc degeneration (IDD) is an important cause of low back pain. Recent evidence suggests that in addition to abnormal and excessive mechanical loading, inflammation may be a key driver for both IDD and low back pain. Obesity, a known mechanical risk factor of IDD, is now increasingly being recognized as a systemic inflammatory state with adipokines being postulated as likely inflammatory mediators. The aim of this review was to summarize the current literature regarding the inflammatory role of adipokines in the pathophysiology of IDD. A systematic literature search was performed using the OVID Medline, EMBASE and PubMed databases to identify all studies assessing IDD and adipokines. Fifteen studies were included in the present review. Leptin was the most commonly assessed adipokine. Ten of 15 studies were conducted in humans; three in rats and two in both humans and rats. Studies focused on a variety of topics ranging from receptor identification, pathway analysis, genetic associations, and proteonomics. Currently, data from both human and animal experiments demonstrate significant effects of leptin and adiponectin on the internal milieu of intervertebral discs. However, future studies are needed to determine the molecular pathway relationships between adipokines in the pathophysiology of IDD as avenues for future therapeutic targets.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".