Proceedings of the 2023 Santa Fe Bone Symposium: Progress and Controversies in the Management of Patients with Skeletal Diseases
Bibliographic record
Abstract
The Santa Fe Bone Symposium (SFBS) held its 23rd annual event on August 5-6, 2023, in Santa Fe, New Mexico, USA. Attendees participated in-person and remotely, representing many states and countries. The program included plenary presentations, panel discussions, satellite symposia, a Project ECHO workshop, and a session on healthcare policy and reimbursement for fracture liaison programs. A broad range of topics were addressed, including transitions of osteoporosis treatments over a lifetime; controversies in vitamin D; update on Official Positions of the International Society for Clinical Densitometry; spine surgery and bone health; clinical applications of bone turnover markers; basic bone biology for clinicians; premenopausal-, pregnancy-, and lactation-associated osteoporosis; cancer treatment induced bone loss in patients with breast cancer and prostate cancer; genetic testing for skeletal diseases; and an update on nutrition and bone health. There were also sessions on rare bone diseases, including managing patients with hypophosphatasia; treatment of X-linked hypophosphatemia; and assessment and treatment of patients with hypoparathyroidism. There were oral presentations of abstracts by endocrinology fellows selected from those who participated in the Santa Fe Fellows Workshop on Metabolic Bone Diseases, held the 2 days prior to the SFBS. These proceedings of the 2023 SFBS present the clinical highlights and insights generated from many formal and informal discussions in Santa Fe.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".