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Record W2811401940 · doi:10.1016/j.jfma.2018.06.010

First tidal volume greater than 8 mL/kg is associated with increased mortality in complicated influenza infection with acute respiratory distress syndrome

2018· article· en· W2811401940 on OpenAlexaff
Ming-Cheng Chan, Wen‐Cheng Chao, Shinn‐Jye Liang, Chien‐Hua Tseng, Hao‐Chien Wang, Ying‐Chun Chien, Kuang‐Yao Yang, Wei‐Chih Chen, Wann‐Cherng Perng, Kuo‐Chin Kao, Han‐Chung Hu, Chau‐Chyun Sheu, Wei‐An Chang, Wen‐Feng Fang, Yu‐Mu Chen, Arthur S. Slutsky, Chieh‐Liang Wu

Bibliographic record

VenueJournal of the Formosan Medical Association · 2018
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersTri-Service General HospitalChang Gung Medical FoundationTaichung Veterans General HospitalNational Taiwan University HospitalKaohsiung Chang Gung Memorial HospitalKaohsiung Medical University Chung-Ho Memorial HospitalChina Medical University Hospital
KeywordsMedicineARDSHazard ratioIntubationMortality rateProportional hazards modelInternal medicineConfidence intervalRespiratory distressEmergency medicineSurgeryLung

Abstract

fetched live from OpenAlex

BACKGROUNDS: Severe influenza infection causes substantial morbidity and mortality worldwide and remains an important threat to global health. This study addressed factors related to treatment outcomes in subjects of complicated influenza infection with acute respiratory distress syndrome (ARDS) during the Taiwan epidemic in the Spring of 2016. METHODS: This is a retrospective study conducted by Taiwan Severe Influenza Research Consortium (TSIRC), including eight tertiary referral medical centers. Patients with virology-proven influenza infection admitted to intensive care unit (ICU) between January and March 2016 were included for analysis. RESULTS: was associated with 26.1% increase in 30-day mortality (adjusted hazard ratio 1.261, 95% confidence interval [CI] 1.072-1.484, p < 0.01). CONCLUSION: may be important in reducing mortality.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.334
Teacher spread0.293 · 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

Citations29
Published2018
Admission routes1
Has abstractyes

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