The use of alkaline phosphatase as a bone turnover marker after spinal cord injury: A scoping review of human and animal studies
Bibliographic record
Abstract
BACKGROUND: Serum alkaline phosphatase (ALP) is measured as an indicator of bone or liver disease. Bone-specific alkaline phosphatase (B-ALP) is an isoform of ALP found in the bone tissue which can predict fractures and heterotopic ossification. OBJECTIVE: The aim of this scoping review was to explore the current use of ALP and B-ALP in studies using humans or animal models of SCI, and to identify ways to advance future research using ALP and B-ALP as a bone marker after SCI. RESULTS: HUMAN STUDIES: 42 studies were included. The evidence regarding changes or differences in ALP levels in individuals with SCI compared to controls is conflicting. For example, a negative correlation between B-ALP and total femur BMD was observed in only one of three studies examining the association. B-ALP seemed to increase after administration of teriparatide, and to decrease after treatment with denosumab. The effects of exercise on ALP and B-ALP levels are heterogeneous and depend on the type of exercise performed. ANIMAL STUDIES: 11 studies were included. There is uncertainty regarding the response of ALP or B-ALP levels after SCI; levels increased after some interventions, including vibration protocols, curcumin supplementation, cycles in electromagnetic field or hyperbaric chamber. Calcitonin or bisphosphonate administration did not affect ALP levels. CONCLUSION: Researchers are encouraged to measure the bone-specific isoform of ALP rather than total ALP in future studies in humans of animal models of SCI.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".