The Radiology of Osteoporotic Vertebral Fractures Revisited
Bibliographic record
Abstract
Until recently there has been little evidence available to validate any method by which to make an accurate diagnosis of an osteoporotic vertebral fractures (OVFs) from plain radiographs. In part this reflects a lack of a completely satisfactory "gold standard," but primarily it relates to the absence of well-designed prospective studies in this context. Historically, OVFs were recognized by evidence of macroscopic structural failure in vertebrae using the criteria applied elsewhere in the skeleton. This comprised altered alignment, fragmentation, cortical disruptions, and breaks, among other changes. However, these morphological criteria were replaced by vertebral morphometry, referring to the use of quantitative or quasi-quantitative measurement tools for fracture diagnosis. Vertebral morphometry emerged as an understanding of and treatment for osteoporosis evolved, mainly in response to the need for expeditious assessments of large numbers of spine images for epidemiological and pharmaceutical purposes. Although most of the descriptions of such morphometric tools have stressed that they were not to be applied to clinical diagnosis with respect to individual patients, this constraint has been widely disregarded. Here we review the major attempts to develop a diagnostic strategy for OVF and describe their characteristics in adults and children. Recent evidence suggests that morphometric (quantitative; ie, based on measurement of dimensions and shape description) criteria are inferior to morphologic (qualitative; ie, based on structural integrity) vertebral damage assessment in identifying people with low bone density and at an increased risk of future fracture. Thus there is now an evidentiary basis for suggesting that morphological assessment is the preferred strategy for use in diagnosing OVF from radiographs. © 2019 American Society for Bone and Mineral Research.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".