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Cost-effectiveness of dual-mobility components in patients with displaced femoral neck fractures

2021· article· en· W3216041286 on OpenAlexaffabout

Bibliographic record

VenueThe Bone & Joint Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsAlberta Bone and Joint Health InstituteUniversity of CalgaryUniversity of Toronto
Fundersnot available
KeywordsFemoral neckAdverse effectClinical judgementJudgementFracture (geology)Complication

Abstract

fetched live from OpenAlex

Aims Total hip arthroplasty (THA) with dual-mobility components (DM-THA) has been shown to decrease the risk of dislocation in the setting of a displaced neck of femur fracture compared to conventional single-bearing THA (SB-THA). This study assesses if the clinical benefit of a reduced dislocation rate can justify the incremental cost increase of DM-THA compared to SB-THA. Methods Costs and benefits were established for patients aged 75 to 79 years over a five-year time period in the base case from the Canadian Health Payer’s perspective. One-way and probabilistic sensitivity analysis assessed the robustness of the base case model conclusions. Results DM-THA was found to be cost-effective, with an estimated incremental cost-effectiveness ratio (ICER) of CAD $46,556 (£27,074) per quality-adjusted life year (QALY). Sensitivity analysis revealed DM-THA was not cost-effective across all age groups in the first two years. DM-THA becomes cost-effective for those aged under 80 years at time periods from five to 15 years, but was not cost-effective for those aged 80 years and over at any timepoint. To be cost-effective at ten years in the base case, DM-THA must reduce the risk of dislocation compared to SB-THA by at least 62%. Probabilistic sensitivity analysis showed DM-THA was 58% likely to be cost-effective in the base case. Conclusion Treating patients with a displaced femoral neck fracture using DM-THA components may be cost-effective compared to SB-THA in patients aged under 80 years. However, future research will help determine if the modelled rates of adverse events hold true. Surgeons should continue to use clinical judgement and consider individual patients’ physiological age and risk factors for dislocation. Cite this article: Bone Joint J 2021;103-B(12):1783–1790.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.297
Teacher spread0.262 · 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

Citations19
Published2021
Admission routes2
Has abstractyes

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