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Record W3161482613 · doi:10.1097/md.0000000000025977

Correlation between serum levels of C-reactive protein and neonatal pneumonia

2021· article· en· W3161482613 on OpenAlexaboutno aff
Xiaowen Li, Zhong Chen

Bibliographic record

VenueMedicine · 2021
Typearticle
Languageen
FieldMedicine
TopicNeonatal and Maternal Infections
Canadian institutionsnot available
FundersSichuan University
KeywordsMedicinePneumoniaC-reactive proteinCorrelationInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Few studies have reported the correlation between serum levels of C-reactive protein (CRP) and neonatal pneumonia. The purpose of the present meta-analysis was to clarify whether an increased serum level of CRP accelerates the development of neonatal pneumonia. METHODS: This protocol is conducted according to the preferred reporting items for systematic reviews and meta-analysis protocol (PRISMA-P) statement guidelines. Related articles were identified by searching PubMed, Embase, Cochrane Library, Web of Science, Science Direct, and CNKI databases. Two investigators extracted information according to the selection criteria and used a set of predefined criteria based on the Newcastle-Ottawa Scale (NOS) criteria to assess the studies. All calculations were carried out with Stata 12.0 (Stata Corp, College Station, TX). RESULTS: The results of this systematic review and meta-analysis will be published in a peer-reviewed journal. CONCLUSION: We hypothesized that a higher serum CRP level is closely correlated with the progression of neonatal pneumonia. CRP as a general systemic inflammation biomarker may help clinicians to make difficult therapeutic decisions for neonatal pneumonia. OPEN SCIENCE FRAMEWORK REGISTRATION NUMBER: 10.17605/OSF.IO/RGBMX.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.017
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.052
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.015
Bibliometrics0.0040.005
Science and technology studies0.0000.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.286
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2021
Admission routes1
Has abstractyes

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