Use of regional clinical data to identify veterans for a multi-center osteoporosis electronic consult quality improvement intervention
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.
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 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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".