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Record W4200246644 · doi:10.7326/m21-2625

Safety and Efficiency of Diagnostic Strategies for Ruling Out Pulmonary Embolism in Clinically Relevant Patient Subgroups

2021· review· en· W4200246644 on OpenAlexafffund
Milou A.M. Stals, Toshihiko Takada, Noémie Kraaijpoel, Nick van Es, Harry R. Büller, D. Mark Courtney, Yonathan Freund, Javier Galipienzo, Grégoire Le Gal, Waleed Ghanima, Menno V. Huisman, Jeffrey A. Kline, Karel G.M. Moons, Sameer Parpia, Arnaud Perrier, Marc Righini, Helia Robert‐Ebadi, Pierre‐Marie Roy, Maarten van Smeden, Philip S. Wells, Kerstin de Wit, Geert‐Jan Geersing, Frederikus A. Klok

Bibliographic record

VenueAnnals of Internal Medicine · 2021
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster UniversityQueen's UniversityOttawa HospitalUniversity of Ottawa
FundersAmsterdam Cardiovascular Sciences, Amsterdam University Medical CentersSchool of Medicine, Indiana UniversityNational Heart, Lung, and Blood InstituteUniversity of Texas MD Anderson Cancer CenterLeids Universitair Medisch CentrumUniversiteit LeidenAssistance publique-Hôpitaux de ParisUniversitetet i OsloNederlandse Organisatie voor Wetenschappelijk OnderzoekUniversiteit UtrechtSykehuset ØstfoldUniversity of Texas Southwestern Medical CenterOttawa Hospital Research InstituteMcMaster UniversityUniversity of Ottawa
KeywordsMedicinePulmonary embolismMeta-analysisD-dimerIncidence (geometry)Pre- and post-test probabilityInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: How diagnostic strategies for suspected pulmonary embolism (PE) perform in relevant patient subgroups defined by sex, age, cancer, and previous venous thromboembolism (VTE) is unknown. PURPOSE: To evaluate the safety and efficiency of the Wells and revised Geneva scores combined with fixed and adapted D-dimer thresholds, as well as the YEARS algorithm, for ruling out acute PE in these subgroups. DATA SOURCES: MEDLINE from 1 January 1995 until 1 January 2021. STUDY SELECTION: 16 studies assessing at least 1 diagnostic strategy. DATA EXTRACTION: Individual-patient data from 20 553 patients. DATA SYNTHESIS: Safety was defined as the diagnostic failure rate (the predicted 3-month VTE incidence after exclusion of PE without imaging at baseline). Efficiency was defined as the proportion of individuals classified by the strategy as "PE considered excluded" without imaging tests. Across all strategies, efficiency was highest in patients younger than 40 years (47% to 68%) and lowest in patients aged 80 years or older (6.0% to 23%) or patients with cancer (9.6% to 26%). However, efficiency improved considerably in these subgroups when pretest probability-dependent D-dimer thresholds were applied. Predicted failure rates were highest for strategies with adapted D-dimer thresholds, with failure rates varying between 2% and 4% in the predefined patient subgroups. LIMITATIONS: Between-study differences in scoring predictor items and D-dimer assays, as well as the presence of differential verification bias, in particular for classifying fatal events and subsegmental PE cases, all of which may have led to an overestimation of the predicted failure rates of adapted D-dimer thresholds. CONCLUSION: Overall, all strategies showed acceptable safety, with pretest probability-dependent D-dimer thresholds having not only the highest efficiency but also the highest predicted failure rate. From an efficiency perspective, this individual-patient data meta-analysis supports application of adapted D-dimer thresholds. PRIMARY FUNDING SOURCE: Dutch Research Council. (PROSPERO: CRD42018089366).

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)0.082
metaresearch head score (Gemma)0.265
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.082
Threshold uncertainty score0.432

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0820.265
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0050.003
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.079
GPT teacher head0.402
Teacher spread0.323 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations81
Published2021
Admission routes2
Has abstractyes

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