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Record W103199375

Antibiotic prophylaxis for total joint replacement surgery: results of a survey of Canadian orthopedic surgeons.

2009· article· en· W103199375 on OpenAlexaffabout
Justin de Beer, Danielle Petruccelli, Coleman Rotstein, Brad Weening, Katie Royston, Mitch Winemaker

Bibliographic record

VenuePubMed · 2009
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Infections and Treatments
Canadian institutionsHamilton Health SciencesJuravinski Hospital
Fundersnot available
KeywordsMedicineOrthopedic surgeryPerioperativeAntibiotic prophylaxisTotal joint replacementJoint replacementAntibioticsGeneral surgeryIntensive care medicineArthroplastySurgery
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: The role of perioperative antibiotic prophylaxis in total joint replacement (TJR) surgery is well established. Whereas guidelines have been published in some countries, in Canada controversy persists concerning the best clinical practice for perioperative antibiotic prophylaxis in TJR. METHODS: We conducted a survey of 590 practising orthopedic surgeons performing TJR in Canada to assess current antibiotic prophylaxis practice. The survey included questions pertaining to antibiotic prophylaxis indications, antibiotic choice, dosing, route and timing of administration in the primary and revision arthroplasty setting, as well as postoperative wound drainage evaluation and management. RESULTS: The response rate after 2 mail-outs was 410 of 590 (69.5%). Current antibiotic prophylaxis regimens varied widely among surgeons, underscoring the controversy that exists regarding what constitutes best clinical practice. CONCLUSION: Opinions regarding use of perioperative antibiotic prophylaxis in TJR vary widely among orthopedic surgeons in Canada, illustrating the controversy as to what constitutes best clinical practice. This survey also points to a lack of consensus about the current management of postoperative wound drainage.

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 score0.183
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.059
GPT teacher head0.249
Teacher spread0.190 · 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

Citations37
Published2009
Admission routes2
Has abstractyes

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