MétaCan
Menu
Back to cohort
Record W1552867979 · doi:10.1002/9781119413936.ch35

Antibiotic Cement in Total Knee Arthroplasty

2021· other· en· W1552867979 on OpenAlexaff
Stephen M. Petis, Steven J. MacDonald

Bibliographic record

VenueEvidence-Based Orthopedics · 2021
Typeother
Languageen
FieldMedicine
TopicOrthopedic Infections and Treatments
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsPeriprostheticMedicineBone cementRheumatoid arthritisTotal knee arthroplastyAntibioticsSurgeryOsteoarthritisArthroplastyCementJoint arthroplastyDiabetes mellitusInternal medicinePathology

Abstract

fetched live from OpenAlex

This chapter presents a case scenario of a 65-year-old male with tricompartmental arthritis who undergoes a primary total knee arthroplasty (TKA). His past medical history is remarkable for rheumatoid arthritis, type 2 diabetes mellitus, chronic diabetic nephropathy, and peripheral vascular disease. As the number of procedures performed continues to rise, the burden of periprosthetic joint infection following TKA will rise concomitantly. One of the most commonly reported modes of failure requiring revision after TKA is aseptic loosening. The addition of antibiotics to bone cement is costlier than cement prepared without antibiotics. The cost of antibiotics added to commercially available bone cement ranges from $210 to $500 per batch of cement. The rate of aseptic loosening following TKA with the use ALBC does not differ compared to using plain bone cement. The chapter also provides recommendations for implementing evidence-based practice in the clinical setting.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0120.003

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 head0.034
GPT teacher head0.298
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueEvidence-Based OrthopedicsSame topicOrthopedic Infections and TreatmentsFrench-language works237,207