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Record W2403941730

Molecular characterization of the polymicrobial flora in chronic rhinosinusitis.

2010· article· en· W2403941730 on OpenAlexaff
Marie-France Stephenson, Leandra Endam Mfuna, Scot E. Dowd, Randall D. Wolcott, Jean Barbeau, M Poisson, Garth A. James, Martin Desrosiers

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldMedicine
TopicSinusitis and nasal conditions
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMolecular biologyChronic rhinosinusitisGynecologyBiologyMedicine
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: Conventional cultures have implicated Staphylococcus aureus (SA) and coagulase-negative Staphylococcus (CNS) as principal pathogens in chronic rhinosinusitis (CRS). These results are questioned by recent studies in which molecular probes implicate Haemophilus influenzae instead. OBJECTIVES: To identify all bacterial species present on sinonasal mucosa using molecular culture (bacterial tag-encoded FLX amplicon pyrosequencing [bTEFAP]) and to compare them with those identified with conventional methods. METHODS: A prospective study of 18 patients undergoing endoscopic sinus surgery for CRS and 9 control patients with pituitary adenomas was conducted. Per-operative mucosal biopsies were assessed with bTEFAP by sequencing the species-specific 16S ribosomal deoxyribonucleic acid (DNA) fragment for genetic identification of bacteria and then compared with simultaneous swab culture. RESULTS: Standard cultures showed mainly SA and CNS. Molecular cultures identified up to 20 organisms per sample. Surprisingly, anaerobic species predominated (Diaphorobacter and Peptoniphilus). SA was nevertheless detected in 50%. CONCLUSION: Molecular cultures such as bTEFAP are sensitive tools for bacterial identification in CRS and suggest that anaerobe involvement may be more frequent than presumed.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.007
GPT teacher head0.208
Teacher spread0.200 · 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

Citations155
Published2010
Admission routes1
Has abstractyes

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