MétaCan
Menu
← Back to cohort
Record W2921889916 · doi:10.1093/jcag/gwz006.087

A88 IMAGING UTILIZATION TRENDS IN IBD (1999–2016)

2019· article· en· W2921889916 on OpenAlexaffabout
Daniel J. Low, Tanya Chawla, Christina Diong, Geoffrey C. Nguyen

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversity of TorontoInstitute for Clinical Evaluative SciencesMount Sinai Hospital
Fundersnot available
KeywordsMedicinePelvisUltrasoundRadiologyAbdomenInflammatory bowel diseaseMagnetic resonance imagingIonizing radiationMalignancyUlcerative colitisNuclear medicineDiseaseInternal medicineIrradiation

Abstract

fetched live from OpenAlex

The disease burden in Inflammatory Bowel Disease is significant in the US and Europe. Imaging modalities including CT, MRI and Ultrasound have similar sensitivity and specificity with IBD diagnosis. While the advantages of CT scans include greater accessibility, lower cost, and better spatial resolution, its major disadvantage is ionizing radiation, in which cumulative doses directly increase the risk of malignancy. On the other hand, ultrasound and MRI do not impart ionizing radiation. Ultrasound allows for evaluation of the gastrointestinal tract but is time intensive and operator/patient dependent. MRI provides improved soft tissue resolution, but is costly, time-consuming and has inferior spatial resolution compared to CT. With growing evidence of radiation-related neoplasms, the literature has been sparse in examining changes in imaging utilization. This study examines imaging trends in IBD patients. A provincial (Ontario) database of 72 933 IBD patients (CD 34 448 and UC 38 485) and 729 330 matched controls were examined from 1999–2016. Quarterly rates of CT abdomen/pelvis, MRI, and ultrasound per patient were examined over this period. The absolute and relative change in imaging utilization was examined. The rates of CT abdomen/pelvis per patient per quarter was 199/1000 for CD patients, 103/1000 for UC, and 19/1000 for non-IBD patients in 1999Q2. In 2014Q1, the rates increased to 411/1000 for CD patients, 229/1000 for UC patients, and 84/1000 in non-IBD patients. This represents a 2.07x, 2.22x, and 4.34x increase for CD, UC and non-IBD patients, respectively. The rates of MR abdomen per patient per quarter was 9/1000 for CD patients, 1/1000 for UC patients, and 1/1000 for non-IBD patients in 1999Q2. This is compared to 286/1000 MR abdomen for CD patients, 76/1000 for UC patients, and 14/1000 for non-IBD patients in 2014Q1. This represents a 31.41x, 78.13x, and 23.38x increase for CD, UC and non-IBD patients, respectively. The rates of abdominal ultrasounds per patient per quarter was 145/1000 for CD patients, 97/1000 for UC patients, and 47/1000 for non-IBD patients in 1999Q2. In 2014Q1, abdominal ultrasounds rates were 163/1000 for CD patients, 141/1000 for UC patients, and 75/1000 for non-IBD patients. This represents a 1.12x, 1.46x, and 1.58x increase for CD, UC, and non-IBD patients respectively. At the beginning of the study, CD and UC patients had a higher utilization of CT abdomen/pelvis per quarter, but non-IBD patients had a higher utilization of ultrasound. At the end of the study, the absolute rate of CT abdomen/pelvis utilization per patient was higher than MRI and ultrasound in all groups. All imaging modalities were utilized at a higher rate at the end of the study. The most marked increase in imaging utilization was in MR abdomen in all groups, with a 31.41x, 78.13x, and 23.38x increase for CD, UC and non-IBD patients, respectively. None

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)0.000
metaresearch head score (Gemma)0.003
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 score0.823
Threshold uncertainty score0.357

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.009
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.006
GPT teacher head0.220
Teacher spread0.215 · 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

Citations0
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the Canadian Association of Gastroenterology→Same topicInflammatory Bowel Disease→French-language works237,207→