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Record W3128464485 · doi:10.26443/mjm.v1i1.401

Optimum Radiological Screening Examination for Lumbar Spine

2020· article· en· W3128464485 on OpenAlexaffvenue
Roya Etemad‐Rezai, Paul V. Fenton

Bibliographic record

VenueMcGill Journal of Medicine · 2020
Typearticle
Languageen
FieldEngineering
TopicMedical Imaging and Analysis
Canadian institutionsKingston General Hospital
Fundersnot available
KeywordsPars interarticularisMedicineRadiological weaponOblique caseRadiographySpondylolysisLumbarLumbar spineRadiologyFluoroscopyLumbosacral jointNuclear medicineSpondylolisthesisSurgery

Abstract

fetched live from OpenAlex

A retrospective study was performed to assess the diagnostic contribution of oblique view films of the lumbar spine, to information obtained from anteroposterior (AP) and lateral films, as an initial screening tool for the detection of pars interarticularis defects. Twenty-two cases of lumbar spondylolysis were selected from 243 lumbar spine reports, randomly combined with 40 plain X-rays of normal lumbar spines, and evaluated by radiology residents. The frequency of correctly detecting a pars defect on lateral vs. right and left oblique views was determined. Of the bilateral spondylolyses, 85% were diagnosed on lateral films compared to 35% on oblique radiographs. Both views gave poor diagnostic yield in detecting unilateral pars defects. In evaluating a total of 186 X-rays, an average of 31 oblique films were incorrectly diagnosed, as compared to an average of 14 misdiagnosed lateral films. Considering the low sensitivity associated with the use of oblique view radiography, in addition to the extra cost and significantly increased radiation exposure seen with this procedure, our findings indicate that oblique views should be used only for selected patients who might require further investigation. We therefore recommend that the initial lumbosacral radiological evaluation be limited to AP and lateral views.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.949
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.053
GPT teacher head0.275
Teacher spread0.222 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2020
Admission routes2
Has abstractyes

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