MétaCan
Menu
Back to cohort
Record W2611106867 · doi:10.1016/j.cmi.2017.04.025

Clinical predictors and clinical prediction rules to estimate initial patient risk for infective endocarditis in Staphylococcus aureus bacteraemia: a systematic review and meta-analysis

2017· review· en· W2611106867 on OpenAlexafffund
Anthony D. Bai, Arnav Agarwal, Marilyn Steinberg, Adrienne Showler, Lisa Burry, George Tomlinson, Chaim M. Bell, Andrew M. Morris

Bibliographic record

VenueClinical Microbiology and Infection · 2017
Typereview
Languageen
FieldMedicine
TopicInfective Endocarditis Diagnosis and Management
Canadian institutionsInstitute for Clinical Evaluative SciencesSinai Health SystemQueen's UniversityMcMaster UniversityUniversity of TorontoUniversity Health Network
FundersPfizer Canada
KeywordsInfective endocarditisStaphylococcus aureusMeta-analysisEndocarditisMedicineBacteremiaIntensive care medicineStaphylococcal infectionsMicrobiologyInternal medicineBiologyAntibioticsBacteria

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.026
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 score0.014
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.026
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0140.035
Bibliometrics0.0040.005
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.003
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 head0.476
Teacher spread0.353 · 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".

Quick stats

Citations64
Published2017
Admission routes2
Has abstractno

Explore more

Same venueClinical Microbiology and InfectionSame topicInfective Endocarditis Diagnosis and ManagementFrench-language works237,207