Description of a Nurse Practitioner-Led Orthogeriatric Model of Care
Bibliographic record
Abstract
Older adults often present with multiple comorbidities and face significant postoperative complications. This study aimed to describe the role of Nurse Practitioner (NP)-led orthogeriatric services in managing hip fracture patients. We conducted a review of health records of older adults with hip and proximal femoral fractures between July 2017 and June 2018, presenting descriptive statistics on patient characteristics, surgical outcomes, and the involvement of orthogeriatric services. A total of 197 participants were included, with a majority being female (n = 132; 67.0%). Most patients (53.8%; n = 106) had between five and nine pre-existing conditions. Among the 192 patients who underwent surgery, 69.8% (n = 134) experienced up to four surgical complications. The Nurse Practitioner provided care to 89.1% (n = 163) of the patients within the orthogeriatric service, with half of the patients (n = 82) requiring at least five NP interventions to manage complex pre- and postoperative needs. Refining the NP-led model could potentially help reduce the burden on physicians and surgeons in treating complex medical conditions, especially in settings where geriatricians may not be readily available.
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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 | 0.004 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".