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Record W2760409272 · doi:10.1503/cjs.000517

Regional consolidation of orthopedic surgery: impacts on hip fracture surgery access and outcomes

2017· article· en· W2760409272 on OpenAlexaffvenueabout
Sara A. Kreindler, Lanette Siragusa, Éric Bohm, Wendy Rudnick, Colleen Metge

Bibliographic record

VenueCanadian Journal of Surgery · 2017
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsUniversity of ManitobaSt. Boniface HospitalGeorge & Fay Yee Centre for Healthcare InnovationManitoba HealthWinnipeg Regional Health Authority
Fundersnot available
KeywordsMedicineOrthopedic surgeryReferralReceiptHip fractureTrauma surgeryEmergency medicineGeneral surgerySurgeryFamily medicineInternal medicineOsteoporosis

Abstract

fetched live from OpenAlex

BACKGROUND: Timely access to orthopedic trauma surgery is essential for optimal outcomes. Regionalization of some types of surgery has shown positive effects on access, timeliness and outcomes. We investigated how the consolidation of orthopedic surgery in 1 Canadian health region affected patients requiring hip fracture surgery. METHODS: We retrieved administrative data on all regional emergency department visits for lower-extremity injury and all linked inpatient stays from January 2010 through March 2013, identifying 1885 hip-fracture surgeries. Statistical process control and interrupted time series analysis controlling for demographics and comorbidities were used to assess impacts on access (receipt of surgery within 48-h benchmark) and surgical outcomes (complications, in-hospital/30-d mortality, length of stay). RESULTS: There was a significant increase in the proportion of patients receiving surgery within the benchmark. Complication rates did not change, but there appeared to be some decrease in mortality (significant at 6 mo). Length of stay increased at a hospital that experienced a major increase in patient volume, perhaps reflecting challenges associated with patient flow. CONCLUSION: Regionalization appeared to improve the timeliness of surgery and may have reduced mortality. The specific features of the present consolidation (including pre-existing interhospital performance variation and the introduction of daytime slates at the referral hospital) should be considered when interpreting the findings.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.090
GPT teacher head0.331
Teacher spread0.241 · 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 designObservational
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

Citations9
Published2017
Admission routes3
Has abstractyes

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