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Proportion of simulations detecting differences between indicators in predicting resistance incidence rates, for ten combinations and five durations.

2016· other· en· W6961155012 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2016
Typeother
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsnot available
Fundersnot available
KeywordsIntensive careIncidence (geometry)Intensive care unitMEDLINECritically ill

Abstract

fetched live from OpenAlex

<p>A) Network of ICUs similar to SPIN-BACTOT’s ICU network, comparing the two most accurate indicators. B) Network of ICUs similar to SPIN-BACTOT’s ICU network, comparing the most accurate indicator to the least accurate. C) Network of ICUs similar to the NHSN, comparing the two most accurate indicators. 3GC: third-generation cephalosporins; amino: aminoglycosides; ARC: aminoglycoside-resistant coliforms; CREKP: carbapenem-resistant <i>E</i>. <i>coli</i>, <i>Klebsiella</i> sp. and <i>Proteus</i> sp.; CRP: carbapenem-resistant <i>Pseudomonas</i> sp.; ICU: intensive care unit; MRSA: methicillin-resistant <i>Staphylococcus aureus</i>; NHSN: National Healthcare Security Network; pip-tazo: piperacillin-tazobactam; PTRC: piperacillin-tazobactam-resistant coliforms; PTRP: piperacillin-tazobactam-resistant <i>Pseudomonas</i> sp.; QRC: quinolone-resistant coliforms; QRP: quinolone-resistant <i>Pseudomonas</i> sp.; SPIN-BACTOT: Québec healthcare-associated bloodstream infections network.</p>

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)0.001
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.888
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.019
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.110
GPT teacher head0.399
Teacher spread0.288 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2016
Admission routes1
Has abstractyes

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