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Record W2761519718 · doi:10.1186/s12879-017-2797-7

Rates of, and risk factors for, septic arthritis in patients with invasive pneumococcal disease: prospective cohort study

2017· article· en· W2761519718 on OpenAlexafffundabout
Thomas J. Marrie, Gregory J. Tyrrell, Sumit R. Majumdar, Dean T. Eurich

Bibliographic record

VenueBMC Infectious Diseases · 2017
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsUniversity of AlbertaProvincial Laboratory of Public HealthDalhousie University
FundersMerck CanadaGovernment of CanadaBaylor UniversityPfizerEli Lilly and Company
KeywordsSeptic arthritisMedicineArthritisBacteremiaInternal medicineDiseaseProspective cohort study

Abstract

fetched live from OpenAlex

BACKGROUND: There are many case reports of septic arthritis complicating invasive pneumococcal disease (IPD); however, no study has compared patients with IPD with septic arthritis to those who didn't develop septic arthritis Thus, we aimed to determine the rates of, and risk factors for, septic arthritis in patients with invasive pneumococcal disease (IPD). METHODS: Socio-demographic, clinical, and serological data were captured on all patients with IPD in Northern Alberta, Canada from 2000 to 2014. Septic arthritis was identified by attending physicians. Descriptive statistics and multivariate analyses were used to compare characteristics of those with septic arthritis and IPD to those who did not. RESULTS: Septic arthritis developed in 51 of 3251 (1.6%) of patients with IPD. Inability to walk independently, male sex, and underlying joint disease were risk factors for developing septic arthritis in patients with IPD. Capsular serotypes 22 and 12F were more common in patients with septic arthritis than those without. CONCLUSIONS: In patients with IPD, septic arthritis is uncommon. Certain risk factors such as walking with or without assistance and underlying joint disease make biological sense as damaged joints are more likely to be infected in the presence of bacteremia. TRIAL REGISTRATION: Not applicable.

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)0.001
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 score0.008
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.274
Teacher spread0.263 · 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

Citations10
Published2017
Admission routes3
Has abstractyes

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