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Record W4366660170 · doi:10.5267/j.ccl.2023.3.007

Effective assessment model for improving capacity of diagnosis and early treatment of st-advanced miscellaneous immediate patients: The case in Vietnam

2023· article· en· W4366660170 on OpenAlexvenueno aff
Nguyen Huy Loi, Pham Manh Hung, Duong Dinh Chinh, Pham Hong Phuong

Bibliographic record

VenueCurrent Chemistry Letters · 2023
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsConventional PCIMedicineReplicateIntervention (counseling)Percutaneous coronary interventionEmergency medicineInternal medicineMyocardial infarction

Abstract

fetched live from OpenAlex

We conducted an initial evaluation of the effectiveness of the application of the model of capacity building for early diagnosis and treatment with PCI in STEMI patients in Nghe An to contribute to solving the problems and to be able to replicate the model. STEMI patients received PCI from 7/2018 - 8/2020 at Nghe An General Hospital. Retrospective and prospective cross-sectional study, intervention with comparison before and after intervention included 280 patients, mean age 71.9 ± 14.59 (years); men accounted for 69.3%. After implementing the model, the number of patients increased by 135%, the time of the door - the ball decreased (71.3 ± 71.8 compared to 152.29 ± 167.3 minutes), the length of hospital stays, and the mortality rate decreased significantly.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.027
GPT teacher head0.305
Teacher spread0.278 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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