MétaCan
Menu
Back to cohort
Record W3013948509 · doi:10.5152/dir.2019.19391

Abdominal and pelvic radiographs of medical devices and materials- part 2: neurologic and genitourinary devices and materials

2020· review· en· W3013948509 on OpenAlexaff
Rishi Philip Mathew, Medica Sam, Timothy Alexander, Vimal Patel, Gavin Low

Bibliographic record

VenueDiagnostic and Interventional Radiology · 2020
Typereview
Languageen
FieldMedicine
TopicPelvic and Acetabular Injuries
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicinePelvisAbdomenRadiographyRadiologyGenitourinary systemMedical physicsConfusionAnatomy

Abstract

fetched live from OpenAlex

Radiographs of the abdomen and pelvis are routinely obtained as a standard part of clinical care for the abdomen and pelvis. Brisk advances in technology over the last few decades have resulted in a multitude of medical devices and materials. Recognizing and evaluating these devices on abdominal and pelvic radiographs are critical, yet increasingly a difficult endeavor. In addition, multiple devices serving different purposes may have a similar radiographic appearance and position causing confusion for the interpreting radiologist. The role of the radiologist is to not only identify accurately these medical objects, but also to confirm for their accurate placement and to recognize any complications that could affect patient care, management or even be potentially life threatening. An extensive online search of literature showed our review article to be the most comprehensive work on medical devices and materials of the abdomen and pelvis, and in this second part of our two-part series, we discuss in depth about the neurologic and genitourinary devices seen on abdominal and pelvic radiographs.

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)0.003
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: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.006
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.031
GPT teacher head0.333
Teacher spread0.302 · 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
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

Citations9
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueDiagnostic and Interventional RadiologySame topicPelvic and Acetabular InjuriesFrench-language works237,207