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Record W4362521256 · doi:10.1007/s00198-022-06640-3

Post hip fracture orthogeriatric care—a Canadian position paper addressing challenges in care and strategies to meet quality indicators

2023· review· en· W4362521256 on OpenAlexafffundabout
Aliya Khan, Hajar AbuAlrob, Hatim Al-Alwani, Dalal S. Ali, Khulod Almonaei, Farah Alsarraf, Earl R. Bogoch, Karel Dandurand, Aaron Gazendam, Angela Juby, Wasim Mansoor, Sharon Marr, Emmett Morgante, Frank Myslik, Emil H. Schemitsch, Prism Schneider, Jenny Thain, Αλεξάνδρα Παπαϊωάννου, Paul Zalzal

Bibliographic record

VenueOsteoporosis International · 2023
Typereview
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsWestern UniversityTrillium Health CentreUniversity of AlbertaUniversity of TorontoMcMaster UniversityUniversity of CalgaryImpactMcMaster University Medical Centre
FundersOsteoporosis CanadaAmgen
KeywordsMedicineOrthopedic surgeryRheumatologyHip fracturePosition (finance)Quality (philosophy)Physical therapyIntensive care medicineInternal medicineOsteoporosisSurgery

Abstract

fetched live from OpenAlex

INTRODUCTION: Osteoporosis is a major disease state associated with significant morbidity, mortality, and health care costs. Less than half of the individuals sustaining a low energy hip fracture are diagnosed and treated for the underlying osteoporosis. OBJECTIVE: A multidisciplinary Canadian hip fracture working group has developed practical recommendations to meet Canadian quality indicators in post hip fracture care. METHODS: A comprehensive narrative review was conducted to identify and synthesize key articles on post hip fracture orthogeriatric care for each of the individual sections and develop recommendations. These recommendations are based on the best evidence available today. CONCLUSION: Recommendations are anticipated to reduce recurrent fractures, improve mobility and healthcare outcomes post hip fracture, and reduce healthcare costs. Key messages to enhance postoperative care are also provided.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.964
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.071
GPT teacher head0.387
Teacher spread0.316 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations15
Published2023
Admission routes3
Has abstractyes

Explore more

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