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Record W2607260666 · doi:10.20381/ruor-20242

Optimization of Lung Scintigraphy in Pregnant Women at The Ottawa Hospital

2017· dissertation· en· W2607260666 on OpenAlexaboutno aff
Mohammad Golfam

Bibliographic record

VenueuO Research (University of Ottawa) · 2017
Typedissertation
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLungScintigraphyObstetricsNuclear medicineMedical physicsGynecologyInternal medicine

Abstract

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INTRODUCTION: Pulmonary embolism (PE) is a major cause of mortality during pregnancy. It is estimated that about 20% of maternal deaths in north america are due to PE. A lung V/Q study in a standard (non-gravid) patient typically consists of a low dosage ventilation study followed by a higher dosage perfusion study. In some centers however, perfusion-only imaging, without accompanying ventilation imaging has been employed. In this method, a several-fold lower dose of radioactivity is used. Perfusion-only imaging has multiple advantages. In addition to reduction of radiation dose to the mother and the fetus, there is decreased cost to the health-care system as well as improved patient convenience and shortened hospital workflow. OBJECTIVES: The present study aimed at assessing the negative predictive value (among other diagnostic accuracy measures) of perfusion-only imaging in a large group of pregnant patients with suspected pulmonary embolism. METHODS: This study was a retrospective cohort study of the entire pregnant patients with suspected PE who underwent V/Q scan at The Ottawa Hospital and their V/Q scans were available in the PACS system. After acquiring REB approval, a comprehensive search in the PACS (Picture Archiving and Communication System) was conducted to find pregnant patients who were assessed for PE in our division since 2004 (the earliest date the V/Q images were available in our system). A statistical consultation was made before the initiation of data collection and at the time of data analysis. All patients who met the inclusion criteria were included. Initially a nuclear medicine resident with 2 years of experience read all the perfusion- only images. The PISAPED criteria were used for image interpretation. Then the results were compared against the reports made by nuclear medicine staffs that were available to us in our electronic system and a final interpretation was made after such comparison. The follow-up clinical notes were used as the gold standard to make a final diagnosis of PE. Finally, diagnostic accuracy measures were calculated. RESULTS: A total of 364 patients were included. Mean maternal age at the time of lung V/Q scan was 30.3 years-old (SD=5.8) ranging from 16 to 51 years-old. From a total of 362 lung perfusion scans, 316/362 (87.3%) scans interpreted as normal, 17/362 (4.7%) scans were interpreted as high probability and 29/362 (8.0%) scans were interpreted as non-diagnostic. Pulmonary embolism was diagnosed in a total of 15 patients directly after performing lung scan. None of the patients with normal perfusion-only scans were diagnosed later with PE, proving a negative predictive value of 100%. The sensitivity and specificity of perfusion-only imaging after including the non-diagnostic studies were 100% (100% to 100%) and 99.1% (88.1% to 94.1%), respectively with a negative predictive value of 100% (100% to 100%) and a positive predictive value of 32.6% (19.1% to 46.2%). Conclusion: The results of the current study show that perfusion-only imaging has a very high negative predictive value for PE in pregnant population and therefore can exclude PE with a very high degree of accuracy.

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)0.001
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.362
Teacher spread0.327 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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