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Record W2067305224 · doi:10.1002/jmri.22303

Device for sectioning prostatectomy specimens to facilitate comparison between histology and in vivo MRI

2010· article· en· W2067305224 on OpenAlexafffund
Bryn Drew, Edward C. Jones, Stefan A. Reinsberg, Andrew Yung, S. Larry Goldenberg, Piotr Kozłowski

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2010
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsProstatectomyHistologyBiomedical engineeringMaterials scienceMedicineAnatomyPathologyProstate

Abstract

fetched live from OpenAlex

PURPOSE: To develop a device for sectioning prostatectomy specimens that would facilitate comparison between histology and in vivo MRI. MATERIALS AND METHODS: A multi-bladed cutting device was developed, which consists of an adjustable box capable of accommodating a prostatectomy specimen up to 85 mm in size in the lateral direction, a "plunger" tool to press on the excised gland from the top to prevent it from rolling or sliding during sectioning, and a multi-bladed knife assembly capable of holding up to 21 blades at 4-mm intervals. The device was tested on a formalin fixed piece of meat and subsequently used to section a prostatectomy specimen. Histology sections were compared with T2-weighted MR images acquired in vivo before the prostatectomy procedure. RESULTS: The prostatectomy specimen slices were very uniform in thickness with each face parallel to the other with no visible sawing marks on the sections by the blades after the cut. MRI and histology comparison showed good correspondence between the two images. CONCLUSION: The developed device allows sectioning of prostatectomy specimens into parallel cuts at a specific orientation and fixed intervals. Such a device is useful in facilitating accurate correlation between histology and MRI data.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.274
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.029
GPT teacher head0.306
Teacher spread0.277 · 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 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

Citations36
Published2010
Admission routes2
Has abstractyes

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