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Record W2902316878 · doi:10.21037/jtd.2018.11.74

Improving clinical outcomes in sepsis and multiple organ dysfunction through precision medicine

2019· editorial· en· W2902316878 on OpenAlexafffund
Sanjay Mehta, Sean E. Gill

Bibliographic record

VenueJournal of Thoracic Disease · 2019
Typeeditorial
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsWestern UniversityLawson Health Research Institute
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsMedicineSepsisIntensive care medicineOrgan dysfunctionPrecision medicineDiseasePopulationCancerInternal medicinePathology

Abstract

fetched live from OpenAlex

Sepsis is an ancient syndrome, as the term “sipo” (‘‘I rot’’ in Greek) was first used in a medical sense in the poems of Homer (1). Two thousand and seven hundred years later, sepsis remains a serious human disease with significant morbidity and mortality. Moreover, sepsis is increasingly common due to an aging population with multiple co-morbid illnesses that are being more aggressively treated with surgery and multiple complex therapies, including biologic and immunosuppressive therapies such as cancer chemotherapy (2-5).

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)0.006
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation 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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.021
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.001
Science and technology studies0.0020.002
Scholarly communication0.0050.004
Open science0.0030.001
Research integrity0.0100.017
Insufficient payload (model declined to judge)0.0090.006

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 head0.070
GPT teacher head0.444
Teacher spread0.373 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations25
Published2019
Admission routes2
Has abstractyes

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