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Record W2023017538 · doi:10.1097/bot.0b013e31817614dd

Treatment of Displaced Femoral Neck Fractures in the Elderly: A Cost-Benefit Analysis

2009· article· en· W2023017538 on OpenAlexaffabout
Bashar Alolabi, Sohail Bajammal, Janhavi Shirali, Paul J. Karanicolas, Amiram Gafni, Mohit Bhandari

Bibliographic record

VenueJournal of Orthopaedic Trauma · 2009
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsMcMaster UniversityUniversity of CalgaryWestern University
Fundersnot available
KeywordsMedicineSurgeryCost–benefit analysisFemoral neckInternal fixationWillingness to payTotal costInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The optimal treatment for displaced femoral neck fractures in elderly patients is controversial. Compared with hemiarthroplasty (HA), internal fixation (IF) is associated with less operative trauma, bleeding, and possibly lower mortality at the expense of a higher reoperation rate and possibly increased cost. METHODS: We estimated the costs from a third party payer perspective after 1 year of 2 strategies (HA and IF) for the treatment of femoral neck fractures in patients over the age of 60 years. Using a decision board, we elicited patient preferences for the 2 operative approaches and calculated the net benefit using the willingness-to-pay technique. RESULTS: The 1-year projected cost of 1 IF was $18,100, and that of 1 HA was $15,843 (incremental cost of $2257 for each IF). Of 108 participants, 61 (56.5%) chose IF as the preferred treatment option and were willing to pay an average of $3.33 per month to have this option available if needed. In Ontario, the total incremental cost of performing IF in patients that choose it was $64,714,103, and the total societal benefit was $289,263,600, yielding a net benefit of $224,549,497. CONCLUSION: The benefits of IF over HA outweigh the incremental costs from the perspective of a third-party payer. IF should be available to patients that choose it.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
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 head0.020
GPT teacher head0.319
Teacher spread0.299 · 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

Citations30
Published2009
Admission routes2
Has abstractyes

Explore more

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