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Record W4382241433 · doi:10.4103/jrms.jrms_352_22

C-reactive protein, D-dimer, erythrocyte sedimentation rate, and troponin in intensive care unit patients with COVID-19 in Iran

2023· article· en· W4382241433 on OpenAlexaff
Hassan Salehi, Bahram Pakzad, Marzieh Salehi, Saeed Abbasi, MohammadMahdi Salehi, Maryam Kazemi Naeini

Bibliographic record

VenueJournal of Research in Medical Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicBlood properties and coagulation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineErythrocyte sedimentation rateD-dimerIntensive care unitTroponinInternal medicineCoronavirus disease 2019 (COVID-19)Troponin IC-reactive proteinGastroenterologyDiseaseInflammation

Abstract

fetched live from OpenAlex

Background: The coronavirus disease 2019 (COVID-19) pandemic in Iran has led to a lack of intensive care unit (ICU) facilities. This study examines C-reactive protein (CRP), D-dimer, erythrocyte sedimentation rate (ESR), and troponin in ICU patients with COVID-19 in comparison to COVID-19 patients admitted to the wards in Iran. Materials and Methods: In a case–control study, troponin, CRP, ESR, and D-dimer were compared in the case samples of 109 COVID-19 patients admitted to the ICU, and in the control group, 140 COVID-19 patients admitted to the wards. Results: The mean of CRP ( P < 0.001) and D-dimer ( P < 0.001) was higher, whereas troponin ( P < 0.001) was lower in patients admitted to the ICU, but no significant difference was observed between the values of ESR ( P = 0.292) in the two groups. Conclusion: This study showed that the values of CRP and D-dimer were higher in patients admitted to the ICU, but no significant difference was observed between the values of ESR in the two groups.

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)0.004
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 score0.052
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
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 head0.170
GPT teacher head0.454
Teacher spread0.284 · 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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