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Record W4401879381 · doi:10.7759/cureus.67835

Pseudoprogression Following Liver Stereotactic Body Radiotherapy (SBRT) in a Patient With Oligometastatic Leiomyosarcoma: A Case Report

2024· article· en· W4401879381 on OpenAlexaff
Mohamed M. Aly, Shaheer Shahhat, Timothy Nguyen

Bibliographic record

VenueCureus · 2024
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineStereotactic radiotherapyRadiologyRadiosurgeryLeiomyosarcomaMedical physicsRadiation therapy

Abstract

fetched live from OpenAlex

Stereotactic body radiotherapy (SBRT) is a non-invasive form of radiation that has been utilized for oligometastatic malignancies. However, pseudoprogression is a common radiological occurrence following this treatment, which manifests as an increase in tumor size before its reduction. We discuss a case of a 58-year-old female patient who initially presented with uterine leiomyosarcoma. Following surgery and postoperative radiation, she was later found to have solitary liver metastasis after three years of surveillance, which was managed by SBRT. However, on short-term follow-up, the lesion was found to have increased in size, prompting discussion regarding whether the growth was a progression of disease or a secondary effect of treatment. After close follow-up, the tumor continued to shrink until it was no longer visible on imaging. This is the first report discussing pseudoprogression following SBRT in a retroperitoneal leiomyosarcoma patient. It serves as a reminder for clinicians to consider the possibility of pseudoprogression before the failure of therapy.

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)0.003
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.285
Teacher spread0.250 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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