Reply to Ganda and colleagues’ Letter to the Editor regarding “Defining the Key Clinician Skills and Attributes for Competency in Managing Patients with Osteoporosis and Fragility Fractures”
Bibliographic record
Abstract
Dear Editor, We wish to thank Ganda and colleagues for their Letter to the Editor regarding our work. We agree with Ganda et al that much of bone healthcare is provided by general primary care clinicians. Depending on complexity and the comfort level of the primary care clinician, the patient may either be managed solely by them or referred to a subspecialist with more expertise. The clinician who possesses the adequate skills and attributes in managing bone health (be that primary care clinician or a subspecialist) is considered by our definition to have “competency” in managing osteoporosis. Because so much of osteoporosis and post-fracture care is not by traditional specialists, we created a decision rule defining the minimum definition of competency in order to encompass the very broad base of people who can competently deliver care for people with osteoporosis, which may include some primary care clinicians. Diversity comes in many forms and may be characterized by race, ethnicity, sex, medical specialty, geographic region of practice, and years of practice, among others. Our cohort included a representative sample of 6 (20%) general internists or general geriatricians who provide primary healthcare services. In addition, we included 2 (7%) patient stakeholders, 2 (7%) advanced practice providers, 1 (3%) orthopedic surgeon, and 1 (3%) nephrologist. Moreover, almost one-fourth of our participants were private practice providers. Collectively, this exemplifies diverse experience, knowledge, and perspective of clinicians who manage the care of people with osteoporosis. In addition, despite the wide range of panel members with different backgrounds, we observed very high parsimony in the group responses. The overall intraclass correlation coefficient of nearly 0.9 indicates high agreement between panelists, further speaking to the robustness of our findings. Meeting the minimum threshold for competency in our tool is not reliant on certification by the subspecialties rheumatology or endocrinology. Indeed, possession of board certification in a subspecialty accounted for only 1.9 points, which, according to our panel, was valued far less than other categories, such as prescribing practices and routinely initiating osteoporosis workup and treatment monitoring. Using our tool, primary care clinicians can readily reach the minimum threshold of 12 points that corresponds to bone health competency. For instance, a family medicine clinician who conducts osteoporosis workup and treatment monitoring, even without a dedicated bone health clinic (3.9 points), prescribes all osteoporosis medications including anabolic agents (4.8 points), obtains continuing medical education (CME) credits every 2-5 years (1.9 points), and interprets a few DXA scans yearly (1.9 points) would acquire sufficient points (12.5) to surpass the adequate competency threshold defined by our rule. In conclusion, the numeric additive tool we developed can be used to determine adequate competency in managing osteoporosis by clinicians across many different specialties, including primary care clinicians. Our methods to establish these criteria were robust, and our panel of experts included 20% general internists or geriatricians who provide primary healthcare services, in addition to a variety of other specialties and patient advocates, all of whom have vested interest and/or extensive experience in managing osteoporosis. We observed limited differences in opinion among this diverse panel. Last, our final criteria are strongly weighted away from subspecialist designation, with other categories accounting for higher weights and thus placing more emphasis on other categories for inclusion in our definition of bone health competency. We believe that the “osteoporosis care gap” could be addressed by focusing on improved training of specialties (including primary care) in the categories with the highest value in bone health competency. For instance, this may include more dedicated training of clinicians in appropriate workup and management of osteoporosis, expanding knowledge of higher-risk medications such as anabolic agents, and improving proficiency in the detection of osteoporosis through bone mineral density measurement. We encourage others to externally validate the numeric additive point system we developed. If there is evidence that our system results in a systematic bias against a subset of healthcare professionals who care for people with bone disease, we have developed a clear framework to refine a future decision rule. Lesley E. Jackson (Methodology, Writing—original draft), Kenneth G. Saag (Conceptualization, Methodology, Supervision, Writing— review & editing), Sindhu R. Johnson (Methodology, Writing—review & editing), Maria I. Danila (Conceptualization, Methodology, Supervision, Writing—review & editing). L.E.J. received funding support through the Walter B. Frommeyer Jr. Fellowship in Investigative Medicine and the Rheumatology Research Foundation (RRF) Investigator Award. This project was also supported by National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) P30AR072583 (M.I.D., K.G.S.). None declared.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.060 | 0.046 |
| Insufficient payload (model declined to judge) | 0.007 | 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".