Validity of Serum Creatine Kinase as a Measure of Muscle Injury Produced by Lumbar Surgery
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Serum creatine kinase (CK) concentrations have historically been used to investigate muscle disease and serious muscle damage, and there is a growing interest in the potential for a biochemical approach to quantifying skeletal muscle injury occurring in orthopedic surgeries and spinal injuries. The wide availability of CK measurement could foster spinal muscle injury research. However, measurement validity has never been systematically demonstrated in clinical settings. In this study, the validity of serum CK concentration elevation as an index of muscle injury was investigated using lumbar decompression surgery (LDS) as a model. SUBJECTS AND METHODS: Blood samples were obtained from 18 research volunteers drawn from the clinical population undergoing LDS. A baseline sample was taken in the preoperative waiting area. Each subject's highest CK concentration between 12 and 48 hours after surgery was used as the biochemical injury response. The surface area of muscle isolated (incision lengthxdepth) and strained by retraction was obtained for concurrent validity testing against biochemical measurement. RESULTS: The correlation between highest total CK concentration and muscle surface area was moderate (r=0.60) and significant (P<0.01). Correlations between surface area and CK at specific time points, revealed minimal loss of association at 12 hours (r=0.57) and 24 hours (r=0.58), but weaker correlations at 6 hours (r=0.45) and 48 hours (r=0.28) after injury. Analyses for proportions of each isoenzyme making up the total CK revealed that baseline and peak CK consisted almost exclusively of skeletal muscle CK (CK-MM), with minimal representation by heart muscle (CK-MB), and brain (CK-BB). CONCLUSIONS: The findings provide support for the validity of serum CK measurement as an index of skeletal muscle injury caused by LDS, and demonstrate that LDS provides a useful model for measurement testing and development studies.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".