Trabecular Bone Score Preceding and during a 2-Year Follow-Up after Sleeve Gastrectomy: Pitfalls and New Insights
Bibliographic record
Abstract
Bariatric surgery (BS) can have negative effects on bone health. Bone microarchitecture quality evaluation using the trabecular bone score (TBS) has not been described in patients after sleeve gastrectomy (SG). To test the hypothesis that the TBS is clinically useful for this population, we evaluated changes in bone mineral density (BMD) and the TBS in a longitudinal cohort study following SG. The measurements before surgery and after 12 and 24 postoperative months were as follows: weight, height, BMI, waist circumference (WC), BMD and TBS. The results at baseline showed the following: a mean BMI of 43 ± 0.56, TBS of 1.25 ± 0.02, lumbar spine BMD T-score of −0.4 ± 0.93, TBS T-score of −2.30 ± 0.21, significantly lower than BMD-T-score, and associated with a BMD-T-TBS-T gap (T-gap) of −2.05 ± 1.26 (−0.24 ± 0.13). One year after surgery, the TBS had significantly improved (+12.12% ± 1.5), leading to a T-gap of −0.296 ± 0.14, which remained stable at 2 years post-surgery. A correlation analysis revealed a significant negative correlation between the T-gap and WC (r = −0.43 p = 0.004). Our interpretation is that abdominal fat may interfere with image acquisition via increased tissue thickness, leading to a false low TBS at baseline. In conclusion, TBS should be interpreted with caution in patients with obesity and elevated WC. Additionally, we show that after SG, the LS microarchitecture measured using the TBS is partially degraded in up to 25% of patients. Further studies are warranted to assess hip bone microarchitecture changes after bariatric surgery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".