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Call for a Singapore National Action Plan for Sepsis (SNAPS): Stop sepsis, save lives

2024· article· en· W4391399978 on OpenAlex
Ee Ling Goh, Kay Choong See, Wei Ling Chua

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAnnals of the Academy of Medicine Singapore · 2024
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsSepsisMedicineIntensive care medicineHealth careAction planDiseaseAction (physics)ImmunologyEconomic growthInternal medicineEconomics

Abstract

fetched live from OpenAlex

Sepsis is a life-threatening organ dysfunction syndrome caused by a dysregulated host response to an infection.1 It affects up to 48.9 million people globally every year and causes 11 million sepsis-related deaths, accounting for 1 in every 5 deaths worldwide.2 The huge disease burden leads to significant consumption of healthcare resources due to longer hospitalisation and the need for intensive care.3 The resultant economic impact is tremendous; for instance, the 1-year incremental costs of sepsis to the healthcare system in Ontario, Canada approximates CAD 1 billion.3 In addition to the complexity of care required for sepsis, the higher healthcare costs incurred may be explained by the post-sepsis syndrome. Sequelae of sepsis include physical, psychological and medical complications.4

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.588
Threshold uncertainty score0.724

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.374
GPT teacher head0.476
Teacher spread0.102 · 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