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The effect of patient institutional transfer during the interstage period of two-stage treatment for prosthetic knee infection

2019· article· en· W2970828872 on OpenAlexaff
Simon Garceau, Yaniv Warschawski, Omar Dahduli, Ibrahim Alshaygy, Jesse Wolfstadt, David Backstein

Bibliographic record

VenueThe Bone & Joint Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Infections and Treatments
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicineCohortArthroplastySurgeryTotal knee arthroplastyCohort studyStage (stratigraphy)Soft tissueInternal medicine

Abstract

fetched live from OpenAlex

Aims The aim of this study was to assess the effects of transferring patients to a specialized arthroplasty centre between the first and second stages (interstage) of prosthetic joint infection (PJI) of the knee. Patients and Methods A search of our institutional database was performed to identify patients having undergone two-stage revision total knee arthroplasty (TKA) for PJI. Two cohorts were created: continuous care (CC) and transferred care (TC). Baseline characteristics and outcomes were collected and compared between cohorts. Results A total of 137 patients were identified: 105 in the CC cohort (56 men, 49 women; mean age 67.9) and 32 in the TC cohort (17 men, 15 women; mean age 67.8 years). PJI organism virulence was greater in the CC cohort (36.2% vs 15.6%; p = 0.030). TC patients had a higher rate of persisting or recurrent infection (53.6% vs 13.4%; p < 0.001), soft-tissue complications (31.3 vs 14.3%; p = 0.030), and reduced requirement for porous metal augments (78.1% vs 94.3%; p = 0.006). Repeat first stage debridement after transfer led to greater need for plastic surgical procedures (58.3% vs 0.0%; p < 0.001). Conclusion Patient transfer during the interstage of treatment for infected TKA leads to poorer outcomes compared with patients receiving all their treatment at a specialized arthroplasty centre. Cite this article: Bone Joint J 2019;101-B:1087–1092.

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 score0.238
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.009
GPT teacher head0.255
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

Citations23
Published2019
Admission routes1
Has abstractyes

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