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Record W2107533590 · doi:10.1177/230949900801600115

Quality of Life after Infection in Total Joint Replacement

2008· article· en· W2107533590 on OpenAlexaboutno aff
JL Cahill, Bruce Shadbolt, Jennie M. Scarvell, Philip N. Smith

Bibliographic record

VenueJournal of orthopaedic surgery · 2008
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Infections and Treatments
Canadian institutionsnot available
FundersAustralian Orthopaedic Association
KeywordsMedicineQuality of life (healthcare)PeriprostheticVisual analogue scaleDiseaseOsteoarthritisPhysical therapyArthroplastyInternal medicineIntensive care medicineGerontologySurgeryAlternative medicinePathologyNursing

Abstract

fetched live from OpenAlex

PURPOSE: To compare the health-related quality of life and functional outcomes of patients with and without periprosthetic infection after total joint replacement (TJR). METHODS: 62 uncomplicated TJRs and 34 TJRs complicated with deep infection were compared using a visual analogue scale for satisfaction, the Western Ontario and McMaster Universities Osteoarthritis Index, Assessment of Quality of Life, and Short Form-36. RESULTS: Patients with complicated TJR had significantly poorer satisfaction in outcome (p<0.0001) and disease-specific functional outcomes (p<0.0001). Six of the 8 health-related quality-of-life scores were also significantly poorer (p<0.05). These results persisted after controlling for age, sex, and follow-up period in a multiple regression analysis. CONCLUSION: Infection following TJR reduces patient satisfaction and seriously impairs functional health status and health-related quality of life. When hospitals are balancing the costs of preventative measures with the costs of treating infection in TJR, the effect on patients' quality of life must be considered. Our findings argue strongly for allocation of health care resources to minimise the occurrence of infection after TJR.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
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 head0.051
GPT teacher head0.296
Teacher spread0.246 · 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

Citations151
Published2008
Admission routes1
Has abstractyes

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