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Record W2020765504 · doi:10.1186/cc14051

Histopathological changes in septic acute kidney injury in critically ill children: an observational analytical study of postmortem renal biopsies

2014· article· en· W2020765504 on OpenAlexfundno aff
Ramachandran Rameshkumar, Subramanian Mahadevan, RN Ganesh, Vivek Bhat

Bibliographic record

VenueCritical Care · 2014
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsnot available
FundersChildren's Health FoundationConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa e Inovação do Estado de Santa CatarinaFinanciadora de Estudos e ProjetosCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorJapan Society for the Promotion of ScienceRussian Foundation for Basic ResearchLondon Health Sciences Centre
KeywordsMedicineAcute kidney injurySepsisCritically illIntensive care medicineObservational studyIncidence (geometry)Renal replacement therapySeptic shockPathogenesisCritical illnessInternal medicine

Abstract

fetched live from OpenAlex

Critically ill children often manifest acute kidney injury (AKI) as part of their disease process with incidence up to 82% and it is independently associated with poor outcome [ 1 ]. Sepsis is the most common contributing factor and little is known about the pathogenesis of septic AKI. There are no consistent histopathological changes in human or experimental septic AKI [ 2 ]. Hence, understanding of the structural changes associated with its occurrence is therefore important in the management of AKI in critically ill children.

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)0.003
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.117
GPT teacher head0.401
Teacher spread0.284 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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