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Modified shock index and mortality rate of emergency patients

2012· article· en· W2150071979 on OpenAlexaff
Ye-cheng Liu, Jihai Liu, Zhe Fang, Guangliang Shan, Jun Xu, Zhiwei Qi, Huadong Zhu, Zhong Wang, Xuezhong Yu

Bibliographic record

VenueWorld Journal of Emergency Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicTrauma, Hemostasis, Coagulopathy, Resuscitation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineIndex (typography)Shock (circulatory)Mortality rateEmergency medicineInternal medicineWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: This study aimed to determine whether modified shock index (MSI) is associated with mortality that is superior to heart rate, blood pressure, or the shock index (SI) in emergency patients. METHODS: A retrospective database review was performed on 22 161 patients who presented to Peking Union Medical College Hospital Emergency Department and received intravenous fluids from January 1 to December 31, 2009. We gathered data of the patients on age, gender, vital signs, levels of consciousness, presenting complaints, and SI and MSI were calculated for all patients. RESULTS: Multivariate regression analysis was performed to determine the correlation between risk factors and outcome. There is a significant correlation between emergency patient mortality rate and patient's vital signs obtained at the triage desk (HR>120 beats/min, systolic BP<90 mmHg, diastolic BP<60 mmHg). MSI is a stronger predictor of emergency patient mortality compared to heart rate and blood pressure alone, whereas SI does not have a significant correlation with emergency patient mortality rate. CONCLUSION: MSI is a clinically significant predictor of mortality in emergency patients. It may be better than using heart rate and blood pressure alone. SI is not significantly correlated with the mortality rate of the emergency patient.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.074
GPT teacher head0.360
Teacher spread0.286 · 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.

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

Citations169
Published2012
Admission routes1
Has abstractyes

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