Biochemical Measurement of Muscle Injury Created by Lumbar Surgery
Bibliographic record
Abstract
PURPOSES: 1. To determine whether lumbar disc surgery (LS) provides a sufficiently detectable rise in serum creatine kinase (CK) concentration to serve as a model to study biochemical measurement of muscle injury, and 2. To use the model to examine the consistency of the time course of CK concentration changes. METHOD: The study used a repeated measures design. Six women and six men scheduled for LS were recruited. Blood samples were taken in the pre-operative waiting areas, immediately after surgery, at 6 hour intervals until discharge, and at 2, 4, and 6 to 7 days following surgery. Total serum CK was quantified using the Roche Modular to detect enzyme concentration. RESULTS: Following LS, mean Total CK increased from a baseline 50 U/L (SD = 53) to a peak 114 U/L (SD = 32) in women (P < 0.001) and from 183 U/L (SD = 69) to a peak 454 U/L (SD = 173) in men (P < 0.05). Baseline to peak changes in CK exceeded subjects' own baseline fluctuations in all 6 women and all 6 men, and amounted to a mean 6 fold (SD = 4) increase in women and 16 fold (SD = 31) increase in men. While CK concentrations returned to baseline over the observation period in all subjects, time to peak ranged between 9 to 47 hours. CONCLUSIONS: The LS model produced a consistently detectable CK response in both genders. Time to peak is variable indicating a need for multiple serial measures to capture this biochemical injury response.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".