Improvement of Lumbar Bone Mass after Infliximab Therapy in Crohn’s Disease Patients
Bibliographic record
Abstract
BACKGROUND: Patients with Crohn's disease (CD) have a high risk of developing osteoporosis, but the mechanisms underlying bone mass loss are unclear. Elevated proinflammatory cytokines, such as tumour necrosis factor-alpha (TNFalpha), have been implicated in the pathogenesis of bone resorption. AIM: To assess whether suppression of TNFalpha with infliximab treatment has a beneficial effect on lumbar bone mass. METHODS: Adult CD patients who had received infliximab treatment, and who underwent lumbar densitometric evaluation before and during treatment, were selected. Adult CD patients who had never received infliximab treatment were selected as controls. Information regarding age, sex, weight, duration of CD, use of glucocorticoids and bisphosphonates, and signs of disease activity between both densitometric measurements were collected. RESULTS: Data from 45 patients were analyzed. The control group (n=30, mean [+/- SD] 26.7+/-9 years of age) had a significantly higher increase in body weight between both evaluations (6.26%+/-8%) than the infliximab group (n=15, 30.6+/-13 years), which had an increase of 0.3%+/-7.4%. There was a strong correlation between the final weight and lumbar bone mineral content (BMC) in both groups. The infliximab group had a significant increase in lumbar bone area (4.15%+/-6.6%), BMC (12.8%+/-13.6%) and bone mineral density (8.13%+/-7.7%) between both evaluations (interval 22.6+/-11 months) compared with the control group. The increase in BMC in patients who had received infliximab treatment was significant when compared with control patients who had received glucocorticoids (n=8) or had evidence of disease activity (n=13). CONCLUSION: Infliximab therapy improved lumbar bone mass independent of nutritional status. This finding suggests that TNFalpha plays a role in bone loss in CD.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".