Celebrating 50-years: the history and future of the International Society of Bone Morphometry
Bibliographic record
Abstract
The International Society of Bone Morphometry (ISBM) is dedicated to advancing research, education, and clinical practice for osteoporosis and other bone disorders by developing and improving tools for the quantitative imaging and analysis of bone. Its initial core mission was to promote the proper use of morphometric techniques in bone research and to educate and train clinicians and basic scientists in bone morphometry. This article chronicles the evolution of the ISBM and the history and development of bone morphometric techniques for the past 50-years, starting with workshops on bone morphometry in 1973, to the formal incorporation of the ISBM in 1996, to today. We also provide a framework and vision for the coming decades. This effort was led by ISBM presidents Dr Erica L. Scheller (2022-2024) and Dr Thomas J. Wronski (2009-2012) in collaboration with all other living ISBM presidents. Though the underlying techniques and questions have changed over time, the need for standardization of established tools and discovery of novel approaches for bone morphometry remains a constant. The ISBM fulfills this need by providing a forum for the exchange of ideas, with a philosophy that encourages the open discussion of pitfalls and challenges among clinicians, scientists, and industry partners. This facilitates the rapid development and adaptation of tools to meet emerging demands within the field of bone health at a high level.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".