MétaCan
Menu
Back to cohort
Record W2811073408 · doi:10.4037/ajcc2018576

Derivation of a PIRO Score for Prediction of Mortality in Surgical Patients With Intra-Abdominal Sepsis

2018· article· en· W2811073408 on OpenAlexaff
Juan Gabriel Posadas-Calleja, Henry T. Stelfox, André Ferland, Danny J. Zuege, Daniel J. Niven, Luc Berthiaume, Christopher J. Doig

Bibliographic record

VenueAmerican Journal of Critical Care · 2018
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsCARE Canada
Fundersnot available
KeywordsMedicineSepsisSeptic shockOrgan dysfunctionInternal medicineLogistic regressionMortality rateIntensive careIntensive care medicineShock (circulatory)Receiver operating characteristicCohort study

Abstract

fetched live from OpenAlex

BACKGROUND: Mortality in patients with intra-abdominal sepsis remains high. Recognition and classification of patients with sepsis are challenging; about 70% of critical care specialists find the existing definitions confusing and not clinically useful. OBJECTIVE: To assess the usefulness of the predisposition, infection/injury, response, organ dysfunction (PIRO) concept in surgical intensive care patients with severe sepsis or septic shock due to an intra-abdominal source. METHODS: Data from 2005 through 2010 of a prospective observational cohort were reviewed retrospectively. RESULTS: < .001). CONCLUSION: The PIRO score is useful for predicting mortality in patients with surgically related intra-abdominal sepsis.

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)0.003
metaresearch head score (Gemma)0.015
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 score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.055
GPT teacher head0.369
Teacher spread0.313 · 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

Citations18
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Critical CareSame topicSepsis Diagnosis and TreatmentFrench-language works237,207