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Record W2149064616 · doi:10.1177/1076029610389027

Controversies in Diagnosis of Pulmonary Embolism

2010· article· en· W2149064616 on OpenAlexaff
Paul D. Stein, H. Dirk Sostman, James E. Dalen, Dale L. Bailey, Marika Bajc, Samuel Z. Goldhaber, Lawrence R. Goodman, Alexander Gottschalk, Russell D. Hull, Fadi Matta, Massimo Pistolesi, Victor F. Tapson, John G. Weg, Philip S. Wells, Pamela K. Woodard

Bibliographic record

VenueClinical and Applied Thrombosis/Hemostasis · 2010
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsUniversity of OttawaUniversity of Calgary
Fundersnot available
KeywordsPulmonary embolismMedicineRadiologyPulmonary angiographyChest radiographAngiographyScintigraphyIonizing radiationRadiographyMedical physicsNuclear medicineInternal medicine

Abstract

fetched live from OpenAlex

The approach to the diagnosis of acute pulmonary embolism (PE) is under constant revision with advances in technology, noninvasive approaches, and increasing awareness of the risks of ionizing radiation. Optimal approaches in some categories of patients are controversial. Data are insufficient for evidence-based recommendations. Therefore, this survey of investigators in the field was undertaken. Even among experts there were marked differences of opinion regarding the approach to the diagnosis of acute PE. Although CT pulmonary angiography was usually the imaging test of choice, the respondents were keenly aware of the dangers of ionizing radiation. In view of advances in scintigraphic diagnosis since the Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED) trial, ventilation/perfusion (V/Q) lung scans or perfusion scans alone and single photon emission computed tomography (SPECT) V/Q lung scans are often recommended. The choice depends on the patient's age, gender, and complexity of the findings on the plain chest radiograph.

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)0.088
metaresearch head score (Gemma)0.231
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.088
Threshold uncertainty score0.467

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0880.231
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0080.004
Science and technology studies0.0040.019
Scholarly communication0.0070.010
Open science0.0050.005
Research integrity0.0090.014
Insufficient payload (model declined to judge)0.0030.001

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 head0.035
GPT teacher head0.336
Teacher spread0.301 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations30
Published2010
Admission routes1
Has abstractyes

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