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Record W4386074959 · doi:10.37247/pamed5ed.5.23.9

Efficacy of Lavage by Tube under Local Anesthesia versus Arthroscopic Treatment of Acute Septic Arthritis of Native Knee

2023· book-chapter· en· W4386074959 on OpenAlexaboutno aff
Byung Hak Oh, Youn Moo Heo, In UK Yeo, Woo Jin Shin, Hyun Jin Yoo

Bibliographic record

VenueVide Leaf, Hyderabad eBooks · 2023
Typebook-chapter
Languageen
FieldMedicine
TopicOrthopedic Infections and Treatments
Canadian institutionsnot available
FundersKonyang University
KeywordsMedicineSeptic arthritisAnesthesiaTube (container)ArthroscopyArthritisSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Although arthroscopic treatment is a minimally invasive surgery that effectively treats septic arthritis of the knee joint, it requires general or regional anesthesia.This study aimed to compare the clinical results of lavage after tube insertion versus arthroscopic treatment.Patients treated with arthroscopic treatment were included in group I (n = 76), while those treated with lavage by tube were included in group II (n = 34).We investigated the following in all patients: demographics, underlying disorders, initial serum white blood cell (WBC) count, C-reactive protein (CRP) level, synovial fluid WBC and polymorphonuclear cell counts, causative organism, initial Kellgren-Lawrence grade, lavage number, interventional delay, hospitalization days, CRP normalization time, and Western Ontario McMaster Universities Osteoarthritis index scores for clinical outcomes at 3 months postoperative.The mean interventional delay was significantly greater in group I (23.6 ± 15.6 h vs. 8.7 ± 9.3 h, p < 0.001).The www.videleaf.comlavage by tube featured a significantly shorter interventional delay time than arthroscopy, while the CRP decrease rate did not differ between groups.Moreover, lavage by tube showed no significant differences in outcomes, including laboratory results and functional outcomes at 3 months postoperative.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.752
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.024
GPT teacher head0.280
Teacher spread0.256 · 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.

Study designOther design
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

Citations0
Published2023
Admission routes1
Has abstractyes

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