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Record W2972577243 · doi:10.1097/md.0000000000017199

Neutrophil–lymphocyte ratio as an early predictor for patients with acute paraquat poisoning

2019· article· en· W2972577243 on OpenAlexaff
Zong Xun Cao, Ya Qi Song, Wen Bai, Wen Jie Wang, Yong Zhao, Su Li Zhang, Shun Yi Feng

Bibliographic record

VenueMedicine · 2019
Typearticle
Languageen
FieldMedicine
TopicParaquat toxicity studies and treatments
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsMedicineParaquatLymphocyteNeutrophil to lymphocyte ratioImmunologyInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

This retrospective study aimed to investigate whether the neutrophil-lymphocyte ratio (NLR) can be used as an early predictor of 90-day survival in patients with acute paraquat (PQ) poisoning.This study enrolled 105 patients with acute PQ poisoning admitted from May 2012 to May 2018. Kaplan-Meier curve, receiver operating characteristic curve, and Cox proportional hazards regression analyses were used to investigate the predictive value of NLR for 90-day survival of patients with acute PQ poisoning.The 90-day survival rate was 40.95% (43/105). Survivors had lower NLR (P <.001), which was an independent predictor of 90-day survival according to the Cox proportional hazard regression analyses. The area under the NLR curve was 0.842 (95% CI: 0.767-0.917, P <.001) in predicting 90-day survival.Our findings showed that low NLR was a valuable early predictor of 90-day survival in patients with acute PQ poisoning.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.082
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.010
GPT teacher head0.273
Teacher spread0.263 · 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

Citations13
Published2019
Admission routes1
Has abstractyes

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