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Record W2098395872 · doi:10.3389/fgene.2014.00020

Assessing the quality of studies supporting genetic susceptibility and outcomes of ARDS

2014· article· en· W2098395872 on OpenAlexaff
Marialbert Acosta‐Herrera, Maria Pino‐Yanes, Lina Pérez‐Méndez, Jesús Villar, Carlos Flores

Bibliographic record

VenueFrontiers in Genetics · 2014
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsSt. Michael's Hospital
FundersInstituto de Salud Carlos IIIEuropean CommissionFundación Ramón Areces
KeywordsARDSMedicinePopulation stratificationGenetic associationIntensive care medicinePopulationAcute respiratory distressDiseaseGenetic predispositionBiologyEnvironmental healthGeneticsInternal medicineGeneLungGenotypeSingle-nucleotide polymorphism

Abstract

fetched live from OpenAlex

The acute respiratory distress syndrome (ARDS) is a severe inflammatory disease manifested as a result of pulmonary and systemic responses to several insults. It is now well accepted that genetic variation influences these responses. However, little is known about the genes that are responsible for patient susceptibility and outcome of ARDS. Methodological flaws are still abundant among genetic association studies with ARDS and here, we aimed to highlight the quality criteria where the standards have not been reached, to expose the associated genes to facilitate replication attempts, and to provide quick-reference guidance for future studies. We conducted a PubMed search from January 2008 to September 2012 for original articles. Studies were considered if a statistically significant association was declared with either susceptibility or outcomes of all-cause ARDS. Fourteen criteria were used for evaluation and results were compared to those from a previous quality assessment report. Significant improvements affecting study design and statistical analysis were detected. However, major issues such as adjustments for the underlying population stratification and replication studies remain poorly addressed.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.135
GPT teacher head0.454
Teacher spread0.319 · 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 teacher head, 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

Citations24
Published2014
Admission routes1
Has abstractyes

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