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Record W4389029147 · doi:10.1093/ofid/ofad500.761

699. Clinical Outcomes and Management of NAAT-Positive/Toxin-Negative <i>Clostridium difficile</i> Infection: A Systematic Review and Meta-Analysis

2023· review· en· W4389029147 on OpenAlexaff
Connor Prosty, Ryan Hanula, Khaled Katergi, Yves Longtin, Emily G. McDonald, Todd C. Lee

Bibliographic record

VenueOpen Forum Infectious Diseases · 2023
Typereview
Languageen
FieldMedicine
TopicClostridium difficile and Clostridium perfringens research
Canadian institutionsJewish General HospitalMcGill University Health CentreUniversité de MontréalMcGill University
Fundersnot available
KeywordsMedicineClostridium difficileInternal medicineClostridium difficile toxin ANucleic Acid Amplification TestsToxinMicrobiologyGastroenterologyVirologyAntibioticsBiology

Abstract

fetched live from OpenAlex

Abstract Background Standalone nucleic acid amplification tests (NAATs) are increasingly used to diagnose Clostridium difficile infections (CDI), although they may be unable to distinguish colonization from disease. A two-stage algorithm pairing NAAT with a toxin immunoassay (Toxin) has been proposed to improve specificity. We sought to evaluate clinical outcomes among patients testing NAAT+/Toxin+ vs NAAT+/Toxin- by conducting a systematic review and meta-analysis. We further compared outcomes between NAAT+/Toxin- patients who were and were not treated. Methods We searched EMBASE and MEDLINE from inception to September 17, 2022 for articles comparing CDI outcomes among symptomatic patients tested by NAAT and Toxin tests. The risk differences (RD) of all-cause mortality and CDI recurrence were computed by random effects meta-analysis between NAAT+/Toxin+ and NAAT+/Toxin- patients, as well as between treated and untreated NAAT+/Toxin- patients. Results Twenty-three observational studies comprising 11749 patients were included. 30-Day all-cause mortality was not significantly different between NAAT+/Toxin+ and NAAT+/Toxin- patients (8.4% vs 6.8%, respectively; RD=0.2%, 95%CI -2.4, 2.9; I2=52.9%). Recurrence at 60 days was significantly higher among NAAT+/Toxin+ (19.8%) vs NAAT+/Toxin- patients (11.0%) (RD=7.7%, 95%CI=4.6, 10.7, I2=37.4%). Among treated compared to untreated NAAT+/Toxin- patients, the all-cause 30-day mortalities were 5.0% and 14.9%, respectively (RD=-9.5%, 95%CI=-15.0, -3.9, I2=0.0%), but 60-day recurrence was not significantly different (11.6% vs 7.0%, respectively; RD=5.3%, 95%CI -1.7, 12.2; I2=47.0%). Conclusion Compared to NAAT+/Toxin- patients, NAAT+/Toxin+ patients had a greater risk of recurrence at 60 days, but not all-cause mortality. Treatment of NAAT+/Toxin- patients was associated with reduced all-cause mortality, but not recurrence. While subject to the limitations of observational studies including the potential for confounding by indication and immortal time bias, these results suggest that treatment of NAAT+/Toxin- patients may be beneficial. A strategy comparing standalone NAAT or staged testing with up front combined NAAT/Toxin testing could be the topic of a randomized controlled trial to determine cost and efficacy. Disclosures All Authors: No reported disclosures

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)0.008
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0150.035
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.123
GPT teacher head0.438
Teacher spread0.316 · 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 designMeta-analysis
Domainnot available
GenreReview

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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