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Record W2159783272 · doi:10.1148/rg.262055063

MR Pulse Sequences: What Every Radiologist Wants to Know but Is Afraid to Ask

2006· review· en· W2159783272 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRadiographics · 2006
Typereview
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineModality (human–computer interaction)RadiologyMagnetic resonance imagingEcho timeMedical physicsPulse (music)Nuclear medicineArtificial intelligenceComputer scienceOpticsPhysics

Abstract

fetched live from OpenAlex

The use of magnetic resonance (MR) imaging is growing exponentially, in part because of the excellent anatomic and pathologic detail provided by the modality and because of recent technologic advances that have led to faster acquisition times. Radiology residents now are introduced in their 1st year of training to the MR pulse sequences routinely used in clinical imaging, including various spin-echo, gradient-echo, inversion-recovery, echo-planar imaging, and MR angiographic sequences. However, to make optimal use of these techniques, radiologists also need a basic knowledge of the physics of MR imaging, including T1 recovery, T2 and T2* decay, repetition time, echo time, and chemical shift effects. In addition, an understanding of contrast weighting is very helpful to obtain better depiction of specific tissues for the diagnosis of various pathologic processes.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.904
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.387
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