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Record W4402900345 · doi:10.1097/nor.0000000000001055

Description of a Nurse Practitioner-Led Orthogeriatric Model of Care

2024· article· en· W4402900345 on OpenAlexaff
Emma Vaillancourt, Chantal Backman, Chantal Chabot, John Joanisse

Bibliographic record

VenueOrthopaedic Nursing · 2024
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsInstitut du Savoir MontfortMontfort Hospital
Fundersnot available
KeywordsMedicinePsychological interventionNurse practitionersMedical recordHealth careFamily medicinePhysical therapyNursingSurgery

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.294
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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