Use of regional clinical data to identify veterans for a multi-center osteoporosis electronic consult quality improvement intervention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Electronic medical record systems can rapidly identify fracture patients so that healthcare systems can target osteoporosis treatment programs. However, it is not clear what proportion of such patients are actually eligible for treatment. Method: In 3 Veterans Affairs Medical Centers, a secondary fracture prevention electronic screening protocol was developed and proceeded in 3 stages. First, all patients with a fracture-related ICD-9 or CPT code for fracture over the preceding 6 months were identified using a SQL server report run regularly on regional clinical data. Additional data was obtained automatically at this stage, and patients were excluded if they were already on bisphosphonate, their fracture was facial or digital, they did not have a primary care provider, they were under age 50 years, or had died. In a second stage, chart abstraction was completed by the project director. Patients were excluded if their fracture occurred after high-impact trauma, the coded fracture was not confirmed on radiograph, the fracture occurred more than 10 years previously, bone density screening had already been obtained, the fracture was pathologic, the patient was receiving palliative care, or the patient had been offered and declined therapy. In the final stage, remaining patients were referred to a bone specialist who reviewed the medical record and generated an electronic consult to the primary provider that gave recommendations for further evaluation and management consistent with current guidelines. Results: Among 986 screened veterans with ICD9 fracture code within the study period, 841 (85%) were ultimately excluded from further intervention. A majority (n=574, 68%) were excluded in the first, automated screening stage [no primary provider (22%), age under 50 years (38%), already on a bisphosphonate (12%), fracture facial or digital (25%), patient had died (3%)]. Chart abstraction was required to exclude 267 (32%) prior to physician review [high trauma (37%), remote injury or no evidence of fracture (36%), palliative care (9%), other reasons (18%)] One hundred three consults were completed, with 80 (78%) recommending osteoporosis treatment or BMD testing. Conclusion: An electronic screening tool was effective at a regional level in identifying recent fracture patients for secondary osteoporosis intervention, but many (85%) are ultimately not eligible for additional interventions. Most exclusions (68%) can be made without additional chart abstraction.
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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 | 0.006 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it