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Record W3117285827 · doi:10.15173/mumj.v17i1.2305

systematic review of clinical decision rules used for diagnosing pulmonary embolism in the pediatric opulation

2020· article· en· W3117285827 on OpenAlexafffund
April Liu, Laura Nguyen, Mohammed Hassan-Ali, April Kam

Bibliographic record

VenueMcMaster University Medical Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsMedicinePulmonary embolismCINAHLPopulationCohortRetrospective cohort studyProspective cohort studyCohort studyMEDLINEPediatricsIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Objective: This review aims to evaluate the diagnostic accuracy of existing, adult clinical decision tools for pulmonary embolism, in the pediatric population.
 Methods: A systematic search and screening of the Pubmed, Embase, CINAHL, and Cochrane databases was done in January 2018. Studies evaluating the diagnostic accuracy of clinical decision tools and/or risk factors and clinical features for pulmonary embolism in the pediatric population were included. The measures of diagnostic accuracy of clinical decision tools were calculated. The pooled sensitivity and specificity of risk factors were calculated using a bivariate random effects model. All included studies were assessed for quality using QUADAS-2.
 Results: Six studies were included: three case-control and three retrospective cohort studies. We found that no standard clinical decision tool for pulmonary embolism has been evaluated in the pediatric population. As well, adult clinical decision tools have low diagnostic utility in pediatrics.
 Conclusion: Adult clinical decision tools should not be used for pediatric patients. There was no single risk factor or clinical feature displaying reliable sensitivity; however, a central venous line, a recent surgery, or the finding of hemoptysis, all have a positive likelihood ratio greater than two, demonstrating their potential diagnostic utility. Large, prospective cohort studies are needed.
 

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)0.003
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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
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 head0.048
GPT teacher head0.332
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 designSystematic review
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

Citations0
Published2020
Admission routes2
Has abstractyes

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