Response to “Vertebral fracture and intervertebral discs”
Bibliographic record
Abstract
We thank Drs. Adams and Dolan for their interest in our work and the opportunity to further discuss the association between vertebral bone mineral density (BMD) and disc degeneration (DD) in the lumbar spine. Their comments arise from our discussion of study limitations, in which we acknowledged that “the age of our subjects was relatively young and a mean age of 50 years is probably too young to determine the full extent of the association studied.” We went on to speculate that there may be a stronger association between greater vertebral BMD and more severe DD in older spines,1 which they question. This was, of course, only speculation, as the association in the elderly could not be addressed with our data and was based on a number of previous studies of elderly women that revealed an association between greater vertebral BMD and more severe lumbar DD. In one such study of 630 women older than 60 years (73.3 ± 6.9 years), higher dual‐energy X‐ray absorptiometry (DXA) lumbar spine BMD was associated with greater disc space narrowing and Kellgren‐Lawrence DD scores.2 In another study of older women (>60 years, 72.8 ± 6.6 years), greater radiographic disc space narrowing was found to associate with both anterior‐posterior and lateral DXA vertebral BMD measurements.3 An association between greater disc bulging and higher DXA lumbar vertebral BMD was also observed in women aged up to 86 years.4 Such associations, however, have been less studied in men, although a similar association between greater disc space narrowing and lumbar BMD acquired with DXA was observed in a cohort of 250 men (65.4 ± 9.0 years).5
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.040 | 0.028 |
| Insufficient payload (model declined to judge) | 0.019 | 0.013 |
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".