ral ssBioMed CentBMC Geriatrics Open AcceResearch article Closing the osteoporosis care gap – Increased osteoporosis awareness among geriatrics and rehabilitation teams
Bibliographic record
Abstract
Background: A care gap exists between recommendations and practice regarding the diagnosis and treatment of osteoporosis in fracture patients. The current study was designed to determine rates and predictors of in-hospital diagnosis and treatment of osteoporosis in patients admitted with fragility hip fractures, and to assess differences in these rates since the outset of the multipronged "Fracture? Think Osteoporosis " (FTOP) Program, which includes education of geriatrics and rehabilitation teams. Methods: This is a retrospective cohort study conducted with data from two Hamilton, Ontario, university-based tertiary-care hospitals, and represents a follow-up to a previous study conducted 8 years earlier. Data pertaining to all 354 patients, age>/ = 50, admitted between March 2003 and April 2004, inclusive, with a diagnosis of fragility hip fracture were evaluated. Twelve patients were excluded leaving 342 patients for analysis, with 75 % female, mean age 81. Outcomes included: Primary – In-hospital diagnosis of osteoporosis and/or initiation of anti-resorptive treatment ("new osteoporosis diagnosis/treatment"). Secondary – In-hospital mortality, BMD referrals, pre-admission osteoporosis diagnosis and treatment. Results: At admission, 27.8 % of patients had a pre-existing diagnosis of osteoporosis and/or were taking anti-resorptive treatment. Among patients with no previous osteoporosis diagnosis/treatment: 35.7 % received a new diagnosis of
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.035 | 0.004 |
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".