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Record W2338069737 · doi:10.1017/ice.2015.347

Routine Surveillance Versus Independent Assessment by an Outcome Adjudication Committee in Assessing Patients for Sternal Surgical Site Infections After Cardiac Surgery

2016· article· en· W2338069737 on OpenAlexaff
Dominik Mertz, Richard Whitlock, Alicia Y. Kokoszka, Stephanie Smith, Alex Carignan, Muhammad Rehan, Iqbal Jaffer, Ali Alsagheir, Mark Loeb

Bibliographic record

VenueInfection Control and Hospital Epidemiology · 2016
Typearticle
Languageen
FieldMedicine
TopicSurgical site infection prevention
Canadian institutionsThrombosis and Atherosclerosis Research InstituteUniversity of AlbertaPopulation Health Research InstituteUniversité de SherbrookeMcMaster UniversityHamilton Health SciencesMcMaster University Medical Centre
Fundersnot available
KeywordsMedicineAdjudicationCohortSurgical site infectionSurgeryClinical trialInternal medicine

Abstract

fetched live from OpenAlex

Based on a cohort of 966 patients, routine surveillance data were not sufficiently accurate for use in clinical trials investigating surgical site infections. Surveillance data can only be used if adequate 90-day follow-up is provided and if cases identified by surveillance are independently reviewed by a blinded outcome adjudication committee.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.050
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.353
Teacher spread0.327 · 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.

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

Citations2
Published2016
Admission routes1
Has abstractyes

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