Bibliographic record
Abstract
Dr. Tiidus, professor and chair of the department of kinesiology and physical education at Wilfred Laurier University in Waterloo, Ontario, Canada, has constructed a text book to convey topics related to exercise and overuse induced skeletal muscle damage and repair mechanisms and their application. This text book contains 21 chapters, divided into 3 main sections reviewing current research related to the mechanism and repair of muscle damage, muscle damage and repair as it relates to issues in specific populations and critical appraisal of specific interventions that have been utilized for the treatment of muscle damage. Each chapter was well represented and closed with summaries to consolidate the material presented. Section one focused on the physiology of muscle damage and repair. Each chapter effectively described the pathophysiology and histology of muscle damage and repair. However, it is important to note that the majority of research presented focused on animal models and one should take caution when trying to extrapolate this information to humans. Section two described the correlation between muscle damage and repair as it relates to diabetes, hormonal influences, Duchenne muscular dystrophy, changes with aging and workplace injuries. It provided a thorough understanding of the types of pathological and muscular overuse conditions which, can be applied clinically to help identify the mechanism of muscle injury. Section three provided information regarding specific interventions used to treat muscle damage. It provided health practitioners with recent research regarding theory, application and effectiveness of interventions to guide treatment and rehabilitation protocols. This text book provided a suitable description of muscle damage and repair in the format of a narrative literature review. It is advised that the reader should have a background in the topics of cellular biology, histology, immunology, muscle physiology and pathology as the authors assume readers possess basic science knowledge. In conclusion, I would recommend this text to students and health practitioners as it is an efficient reference in understanding the pathophysiology of skeletal muscle damage and repair. However, in terms of treatment modalities I would recommend using a different source as it does not include all aspects of conservative care.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".