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Record W4401011957 · doi:10.1186/s12894-024-01479-8

Stereotactic Body Radiation Therapy (SBRT) in prostate cancer in the presence of hip prosthesis – is it a contraindication? A narrative review

2024· review· en· W4401011957 on OpenAlexaff
Sheen Dube, Vibhay Pareek, Mansi Barthwal, Febin Antony, David Sasaki, Ryan Rivest

Bibliographic record

VenueBMC Urology · 2024
Typereview
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsCancerCare ManitobaUniversity of Winnipeg
Fundersnot available
KeywordsMedicineContraindicationProstate cancerRadiation therapyRadiologyProsthesisNarrative reviewCancerMedical physicsOncologySurgeryInternal medicineIntensive care medicinePathology

Abstract

fetched live from OpenAlex

Hip replacement is a common orthopedic surgery in the aging population. With the rising incidence of prostate cancer, metallic hip prosthetics can cause considerable beam hardening and streak artifacts, leading to difficulty in identifying the target volumes and planning process for radiation treatment. The growing use of Stereotactic Body Radiation Therapy (SBRT) to treat prostate cancer is now well established. However, the use of this treatment modality in the presence of a hip prosthesis is poorly understood. There is enough literature on planning for external beam radiation treatment without any difficulties in the presence of hip prosthesis with conventional or Hypofractionated treatment. However, there is a shortage of literature on the impact of the prosthesis in SBRT planning, and there is a need for further understanding and measures to mitigate the obstacles in planning for SBRT in the presence of hip prosthesis. We present our review of the intricacies that need to be understood while considering SBRT in the presence of hip prostheses in prostate cancer treatment.

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.035
GPT teacher head0.356
Teacher spread0.321 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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