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Septic Arthritis in Immunosuppressed Patients: Do Laboratory Values Help?

2020· article· en· W3013277195 on OpenAlexaff
Jared Bell, Luke Rasmussen, Arun Kumar, Michael G. Heckman, Elizabeth R. Lesser, Joseph L. Whalen, Glenn G. Shi, Benjamin K. Wilke

Bibliographic record

VenueJAAOS Global Research and Reviews · 2020
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Infections and Treatments
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsMedicineSynovial fluidSeptic arthritisErythrocyte sedimentation rateInternal medicineArthritisRetrospective cohort studyCohortGastroenterologyImmunologyPathology

Abstract

fetched live from OpenAlex

Introduction: Previous studies have recommended synovial fluid cell count thresholds of 50,000 cells/mm−3 to diagnose septic arthritis; however, data to support this are limited. It is also unknown if this value is valid in immunosuppressed patients. Methods: We retrospectively reviewed 33 immunosuppressed patients treated at our institution from 2008 to 2018. We compared culture-positive patients with culture-negative patients. Results: We found no statistically significant differences in synovial fluid cell count, percent synovial fluid neutrophils, erythrocyte sedimentation rate, or C-reactive protein between the groups (all P = 0.081). The median synovial fluid cell count in the culture-positive cohort was 29,000 cells/mm−3, with only 31.2% having >50,000 cells/mm−3. Conclusion: Traditional synovial fluid cell thresholds are not a reliable method of diagnosing septic arthritis in immunosuppressed patients.

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)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.058
GPT teacher head0.387
Teacher spread0.329 · 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 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

Citations12
Published2020
Admission routes1
Has abstractyes

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