Estimating Lumbar Spine Least Significant Change for Fewer than Four Vertebrae: The Manitoba BMD Registry
Bibliographic record
Abstract
Introduction : The International Society of Clinical Densitometry recommends omitting lumbar vertebrae affected by structural artifact from spine BMD measurement. Since reporting fewer than 4 vertebrae reduces spine BMD precision, least significant change (LSC) needs to be adjusted upwards when reporting spine BMD change based on fewer than 4 vertebrae . Methodology : In order to simplify estimating LSC from combinations of vertebrae other than L1-L4 (denoted LSC L1-4 ), we analyzed 879 DXA spine scan-pairs from the Manitoba BMD Program's ongoing precision evaluation. The additional impact on the LSC of performing the second scan on the same day vs different day was also assessed. Results : LSC progressively increased when fewer vertebrae were included, and also increased when the scans were performed on different days. We estimated that the LSC L1-4 should be adjusted upwards by 7 %, 24 % and 65 % to approximate the LSC for 3, 2, or 1 vertebral body , respectively. To additionally capture the greater LSC when the precision study was done on different days, LSC L1-4 derived from a precision study where scans were done on the same day should be adjusted upwards by 39 %, 60 % and 112 % for 3, 2, or 1 vertebral body, respectively. Conclusion : LSC L1-4 derived from a precision study where scans are performed on the same day can be used to estimate LSC for fewer than 4 vertebrae and for scans performed on different days.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".