POS0393 DEFINING THE KEY ATTRIBUTES OF A CLINICIAN WITH COMPETENCE IN BONE HEALTH MANAGEMENT
Bibliographic record
Abstract
Background Osteoporosis and fragility fractures are managed by clinicians across a variety of specialties and there is no specific certification in osteoporosis diagnosis and management. These clinicians are an integral part of interventions aimed to improve bone health care (e.g., fracture liaison service [FLS]). Yet, the key skills and attributes of a clinician with competence in bone health management have not been established in a systematic fashion. Objectives We conducted a Delphi exercise and a discrete choice experiment (DCE) to generate a decision rule aiming to define the minimal attributes of a clinician best poised to assess and treat people with osteoporosis and serve as a referral source for post-fracture management. Methods In part 1, we used a modification of the Delphi method with two rounds. Clinicians with experience in treating osteoporosis and representatives of patient advocacy groups were purposively sampled to participate. Participants asynchronously generated a list of desirable characteristics/skills of a “clinician with competence in bone health”. Characteristics were coded and organized into non-overlapping themes or “attributes” with sub-themes or “levels” within each attribute. Participants prioritized and ranked levels in order of perceived importance for inclusion in the definition for a bone health clinician. Levels within attributes associated with the highest median scores were included in the final list of criteria. In part 2, participants ranked 20 hypothetical clinicians defined by various levels of attributes from highest to lowest likelihood of being a bone health clinician to identify the minimal threshold for defining competence in managing bone health. Consistency amongst rankings was evaluated using intraclass correlation coefficients (ICC). In part 3, we conducted a DCE to generate a weighted importance score for each independent and mutually exclusive attribute and level such that the sum of weights across the highest level within each attribute would equal 100%. The threshold for competence was the total weighted score at which ≥70% of participants agreed a clinician had bone health competence. Results Part 1 included 13 participants, and 11 completed the DCE survey. Those who completed part 1 included 3 endocrinologists, 3 rheumatologists, 1 orthopedist, 3 general internists, and 3 representatives of patient advocacy groups. The Delphi exercise generated a list of N=108 characteristics, which were coded and grouped into common themes/attributes. Through an iterative process with 2 rounds of piloting, the attribute categories were reduced to 8 broad categories with a total of 20 levels. The participants’ rankings of the relative probability that each of the 20 hypothetical clinician cases represented a clinician with adequate competence in bone health is plotted in Figure 1. The ICC for agreement across participants was 0.90 (95% confidence interval [CI]: 0.83, 0.95). The maximum possible score in the final criteria was 25. A threshold score of ≥12 classified a clinician as having adequate competence in bone health. For example, a clinician that prescribes all osteoporosis drugs, performs osteoporosis workup/ treatment monitoring, and leads or participates in an FLS would receive 5, 3.5, and 3.5 points, respectively, that summed would reach the threshold. Conclusion We developed a numeric additive decision rule to classify clinicians across multiple specialties with competence in evaluating and treating patients with osteoporosis. Our data provides the critical definition of a “clinician with competence in bone health” that may be useful for identifying and qualifying the skill of clinicians who may be included in interventional studies or clinical activities that aim improve bone health care. REFERENCES: NIL. Acknowledgements: NIL. Disclosure of Interests Lesley Jackson: None declared, Sindhu Johnson: None declared, Ellen McNeeley: None declared, Kenneth Saag Grant/research support from: Amgen, Horizon, LG Chem, Radius, SOBI, Maria Danila Consultant of: UCB, Grant/research support from: Pfizer.
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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 | 0.027 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.072 | 0.021 |
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".