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Record W4402325549 · doi:10.1136/spcare-2024-anzspm.61

P-13 Genomic variation in symptom expression in men with castrate resistant prostate cancer

2024· article· en· W4402325549 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePoster presentations · 2024
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsnot available
Fundersnot available
KeywordsVariation (astronomy)Prostate cancerCancerExpression (computer science)OncologyInternal medicineMedicineComputer scienceAstrophysicsPhysics

Abstract

fetched live from OpenAlex

<h3>Background</h3> Men with castrate resistant prostate cancer (CRPC) suffer from symptoms related to both their disease and its treatment with marked variation between individuals with respect to symptom expression despite similar tumour burdens. This may be related to genetic variation. <h3>Objective</h3> This study aimed to determine whether genetic variation in cytokine expression can predict severity of symptoms in men with CRPC and investigate whether symptom severity was related to tumour burden. <h3>Methods</h3> A prospective, longitudinal consecutive patient cohort study across two Queensland sites. Patient characteristics including tumour burden and current treatment were collected at baseline. Symptom severity was assessed using the Edmonton Symptom Assessment Scale (ESAS-R) 3–4 weekly for up to 6 assessments, with blood taken for genetic analysis once during the study. Cytokine gene variants of each participant were assessed using a panel of SNPs most reported to be associated with symptom variation in the literature. Analysis was performed using R software and the package SNPassoc (R Core team, 2021; Gonzalez JR. et al, 2007). <h3>Results</h3> Twenty-eight of 67 (42%) of participants had a low, and 39/67 (58%) a high tumour burden. Tumour burden remained constant throughout the study period for all participants. Twenty-six of 67 (39%) participants were classified as having low ESAS-R severity and 67 (61%) as high ESAS-R severity. Symptom severity was not related to tumour burden or patient characteristics. One hundred and forty-four SNPs from 63 participants were analysed, of which results were available for 142 SNPs. Fifteen SNPs were significantly associated with symptom severity. In multivariable analysis, SNP rs2069772 in IL2 (p = 0.001), rs1554606 in IL6 (p=0.003), rs2227306 in IL8 (p=0.008) and rs2069718 in IFNG (p=0.001) were highly predictive of symptom severity. <h3>Discussion</h3> Although multiple factors can influence symptom severity, genetic variation may well play a part. The early identification of men likely to develop severe symptoms during the course of their prostate cancer could theoretically enable symptoms to be managed more aggressively from an early stage. Our findings also add to the expanding data bases that document the associations of genetic polymorphism with both disease and symptom expression.

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.

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 score0.475
Threshold uncertainty score0.344

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 head0.025
GPT teacher head0.341
Teacher spread0.316 · 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