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Record W2570864118 · doi:10.21037/atm.2016.11.41

Alleviation of gram-negative bacterial lung inflammation by targeting HECTD2

2016· letter· en· W2570864118 on OpenAlexafffund
Rick Kapur, John W. Semple

Bibliographic record

VenueAnnals of Translational Medicine · 2016
Typeletter
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsSt. Michael's HospitalUniversity of TorontoCanadian Blood Services
FundersHealth CanadaCanadian Blood Services
KeywordsInflammationGramMedicineLungGram-negative bacterial infectionsGram-negative bacteriaImmunologyMicrobiologyBiologyBacteriaAntibioticsInternal medicineEscherichia coliGenetics

Abstract

fetched live from OpenAlex

Lung injury remains a significant clinical problem worldwide. The nature and pathogenesis of the injury is highly multifactorial as it can be acute or chronic, triggered by bacteria, viruses, fungi, transfusions, sepsis, multiple fractures, aspiration and several other factors. In the case of invading pathogens and sepsis, the innate immune system may get overwhelmed, resulting in the secretion of large amounts of proinflammatory cytokines which mediate pulmonary edema, shock and potentially, multi-organ failure (1,2).

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)0.001
metaresearch head score (Gemma)0.002
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0030.003

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 head0.040
GPT teacher head0.328
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 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
GenreCommentary

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 routes2
Has abstractyes

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