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Record W3048274437 · doi:10.14740/jmc3545

Aches and Pains in a Patient With History of Prostatectomy: Extensive Skeletal Metastasis Picked up by Diffusion-Weighted Magnetic Resonance Imaging

2020· article· en· W3048274437 on OpenAlexvenueno aff
Abraham M. Ittyachen, Meera Radhakrishnan, Thomas Kuncheria, Rajeev Anand

Bibliographic record

VenueJournal of Medical Cases · 2020
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineProstate cancerMagnetic resonance imagingProstatectomyMetastasisProstateCancerProstate-specific antigenRadiologyDiffusion MRIUrologyInternal medicine

Abstract

fetched live from OpenAlex

A 64-year-old gentleman presented to the out-patient with complaints of generalized body ache. He had a history of prostate cancer for which robotic radical prostatectomy (RP) was done earlier. The levels of prostate specific antigen (PSA) and alkaline phosphatase (ALP) were significantly elevated. Skeletal radiograph showed only few sclerotic foci. Magnetic resonance imaging whole-body diffusion-weighted image (MRI-WB-DWI) however revealed the presence of diffuse skeletal metastasis. In any elderly male who presents with generalized body pain, eliciting a good history should not be overlooked. With a history of prostate cancer, diffuse metastases should be high in the list of differential diagnosis. Though there are several imaging methods to detect metastases, MRI-WB-DWI is a welcome alternative to the established methods. Patients who undergo RP for prostate cancer should be counselled regarding the importance of follow-up of their PSA levels.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.023
GPT teacher head0.276
Teacher spread0.253 · 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 designCase report
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 routes1
Has abstractyes

Explore more

Same venueJournal of Medical Cases→Same topicProstate Cancer Treatment and Research→French-language works237,207→