Defining the key clinician skills and attributes for competency in managing patients with osteoporosis and fragility fractures
Bibliographic record
Abstract
Osteoporosis and fragility fractures are managed by clinicians across many medical specialties. The key competencies of clinicians delivering bone health care have not been systematically established. We aimed to develop a decision rule to define the threshold of adequate skills and attributes associated with clinical competency in bone health for a clinician serving as a referral source for bone health care. Using a modified-Delphi method, we invited clinicians with expertise in treating osteoporosis and representatives of patient advocacy groups focused on bone health to create a list of desirable characteristics of a clinician with bone health competency. Characteristics were defined as "attributes" with "levels" within each attribute. Participants prioritized levels by perceived importance. To identify the cut points for defining adequate competency, participants next ranked 20 hypothetical clinicians defined by various levels of attributes from highest to lowest likelihood of having adequate bone health competency. Lastly, we conducted a discrete choice experiment (DCE) to generate a weighted score for each attribute/level. The threshold for competency was a priori determined as the total weighted score at which ≥70% of participants agreed a clinician had adequate bone health competency. Thirteen participants generated lists of desirable characteristics, and 30 participants ranked hypothetical scenarios and participated in the DCE. The modified-Delphi exercise generated 108 characteristics, which were reduced to 8 categories with 20 levels with associated points. The maximum possible score was 25 points. A summed threshold score of >12 points classified a clinician as having adequate bone health competency. We developed a numeric additive decision rule to define clinicians across multiple specialties as having adequate competency in managing bone health/osteoporosis. Our data provide a rigorously defined criteria for a clinician with competency in bone health and can be used to quantitate the skills of clinicians participating in bone health research and clinical care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".