Bibliographic record
Abstract
Tendons are extracellular matrix rich structures allowing the transmission of forces generated by skeletal muscles to bones in order to produce movements. Some intrinsic characteristics of tendons, namely hypovascularity and hypocellularity, may explain their slow rate of healing. A growing body of evidence suggests that the inflammatory process, essential for pathogen clearance and injury scavenging, may play opposite functions in tendon healing. For instance, inflammation can lead to degradation of intact collagen and to viable cell death, thereby increasing the functional deficit and recovery period. Paradoxically, many cellular and subcellular events occurring during the inflammatory response lead to the release of a plethora of growth factors that trigger the healing phase. Prostaglandins are implicated in the inflammatory process and may also contribute to the primary steps of tendon healing. Prolonged administration of non steroidal anti-inflammatory drugs (NSAIDs) is a common practice following musculoskeletal injuries. However, there is no clear consensus on the effect of NSAIDs on tendon healing. This review presents a contemporary vision of the inflammatory process following tendon injury and examines the roles of the constitutive and inducible COX-derived prostaglandins. The effect of COX inhibitors will be addressed and special attention will be taken to describe COX-independent effects of these pharmacological inhibitors. Together, this review is an attempt to guide readers toward a more conscientious use of NSAIDs following tendon injuries.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".