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Record W2796460462 · doi:10.1093/cid/cix999

Factors Associated With 30-Day Mortality Rate in Respiratory Infections Caused by Streptococcus pneumoniae

2017· article· en· W2796460462 on OpenAlexaff
Matthew P. Cheng, Isaac I. Bogoch, Karen Green, Agron Plevneshi, Wallis Rudnick, Altynay Shigayeva, Allison McGeer, Todd C. Lee, Mahin Baqi, David Richardson, Abdelbaset Belhaj, Ian Kitai, Danny Z. Chen, Eileen de Villa, Walter Demczuk, Irene Martín, Hani L. N. Dick, James M. Downey, Jeff Powis, Nataly Farshait, King S Lee, Wayne L. Gold, Sharon Walmsley, Frances Jamieson, Jennie Johnstone, Sigmund Krajden, J. Kapala, Kevin Katz, Mark Loeb, Fiona Smaill, Reena Lovinsky, David Rose, Charlotte Ma, Sylvia Pong-Porter, Barbara Willey, Matthew Muller, Sharon O’Grady, Anne Opavsky, Krystyna Ostrowska, Alicia Sarabia, Neil Rau, Susan E. Richardson, Dat Tran, Valérie Sales, Phoebe Shokry, Michael E. Silverman, Andrew E. Simor, Mary Vearncombe, Greg Tyrrell, Aurora Wilson, Barbara Yaffe, Deborah Yamamura

Bibliographic record

VenueClinical Infectious Diseases · 2017
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsMount Sinai HospitalUniversity Health NetworkMcGill University
Fundersnot available
KeywordsMedicineStreptococcus pneumoniaeConfidence intervalHazard ratioInternal medicineAntibioticsMortality rateAntibiotic therapyEmpiric therapyMicrobiologyPathology

Abstract

fetched live from OpenAlex

In multivariable analysis of associations between initial antibiotic therapy and clinical outcomes in 5005 patients with microbiologically confirmed Streptococcus pneumoniae infections, "discordant" empiric antibiotic therapy was not associated with 30-day mortality rate (hazard ratio, 0.94; 95% confidence interval, .67-1.32).

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.005
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.113
GPT teacher head0.404
Teacher spread0.291 · 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

Labeled directly by 2 models reading the full record.

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

Citations3
Published2017
Admission routes1
Has abstractyes

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