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Deficits in preference-based health-related quality of life after complications associated with tibial fracture

2018· article· en· W2888998060 on OpenAlexaff
Ida Leah Gitajn, Alexander Titus, Anna N.A. Tosteson, Sheila Sprague, Kyle J. Jeray, Brad Petrisor, M.F. Swiontkowski, Mohit Bhandari, Gerard P. Slobogean

Bibliographic record

VenueThe Bone & Joint Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineIntramedullary rodQuality of life (healthcare)ComplicationSurgeryQuality-adjusted life yearCost effectiveness

Abstract

fetched live from OpenAlex

Aims: The aims of this study were to quantify health state utility values (HSUVs) after a tibial fracture, investigate the effect of complications, to determine the trajectory in HSUVs that result in these differences and to quantify the quality-adjusted life years (QALYs) experienced by patients. Patients and Methods: This is an analysis of 2138 tibial fractures enrolled in the Fluid Lavage of Open Wounds (FLOW) and Study to Prospectively Evaluate Reamed Intramedullary Nails in Patients with Tibial Fractures (SPRINT) trials. Patients returned for follow-up at two and six weeks and three, six, nine and 12 months. Short-Form Six-Dimension (SF-6D) values were calculated and used to calculate QALYs. Results: Compared with those who did not have a complication, those with a complication treated either nonoperatively or operatively had lower HSUVs at all times after two weeks. The HSUVs improved in all patients with the passage of time. However, they did not return to the remembered baseline preinjury values nor to US age-adjusted normal values by 12 months after the injury. Conclusion: While the acute fracture and complications may have resolved clinically, the detrimental effect on a patient's quality of life persists up to 12 months after the injury. Cite this article: Bone Joint J 2018;100-B:1227-33.

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 score0.035
Threshold uncertainty score0.344

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 head0.085
GPT teacher head0.313
Teacher spread0.228 · 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

Citations21
Published2018
Admission routes1
Has abstractyes

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