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Record W4413615853 · doi:10.1111/anae.16732

Epidemiology, patient outcome and complications after non‐operative management of hip fracture: a systematic review

2025· review· en· W4413615853 on OpenAlexaboutno aff
James M. Winfield, Lynn McNicoll, Iain Moppett

Bibliographic record

VenueAnaesthesia · 2025
Typereview
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineConfoundingHip fractureMeta-analysisEpidemiologyPopulationSurgeryInternal medicineEnvironmental healthOsteoporosis

Abstract

fetched live from OpenAlex

INTRODUCTION: Surgery is the preferred treatment for hip fracture in older people. However, a proportion of patients with hip fracture do not receive surgery. There is a lack of contemporary evidence describing this patient population and what their associated outcomes are. We aimed to describe the variation in non-operative management and its outcomes around the world. METHODS: We performed a systematic review and meta-analysis of older people presenting to hospital with hip fracture, comparing those with and without surgery for non-operative proportions, mortality and other outcomes. Risk of bias was assessed using the Newcastle-Ottawa Scale. We performed a random effects meta-analysis with adjustment for clustering. RESULTS: Of 4437 screened studies, 185 were included from 172 separate cohorts, 44 countries, six continents and involving 10,763,994 patients. The overall proportion of non-operative management was 8.4% (95%CI 7.2-9.7%) with wide within-country and regional variation. There was no consistent association of non-operative management proportions with the admission characteristics of sex, fracture type or patient ethnicity. Non-operative management was associated with a greater relative risk of death at all time points. Risk of bias was generally low except for the expected confounding by indication. DISCUSSION: Non-operative management of hip fracture is relatively common, but there is wide variation that is unexplained by differences in patient characteristics. The evidence is limited by incomplete reporting of patient characteristics and outcomes, and a lack of controlled studies even in the highest risk populations. Further work is needed to understand this decision-making process.

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)0.001
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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.209
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
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 head0.047
GPT teacher head0.398
Teacher spread0.351 · 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 designSystematic review
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

Citations3
Published2025
Admission routes1
Has abstractyes

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