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PD58-06 PATIENTS’ VALUES AND PREFERENCES FOR TREATMENT OF SMALL RENAL MASSES

2024· article· en· W4394809006 on OpenAlexaboutno aff
Clara Diaz, Maryam Kandi, Philippe D. Violette, Gordon Guyatt, Mathieu Gratton, Patrick O. Richard

Bibliographic record

VenueThe Journal of Urology · 2024
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsnot available
Fundersnot available
KeywordsRenal massMedicineUrologyInternal medicineKidney

Abstract

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You have accessJournal of UrologyKidney Cancer: Epidemiology & Evaluation/Staging/Surveillance III (PD58)1 May 2024PD58-06 PATIENTS' VALUES AND PREFERENCES FOR TREATMENT OF SMALL RENAL MASSES Clara Diaz, Maryam Kandi, Philippe Violette, Gordon Guyatt, Mathieu Gratton, and Patrick Richard Clara DiazClara Diaz , Maryam KandiMaryam Kandi , Philippe ViolettePhilippe Violette , Gordon GuyattGordon Guyatt , Mathieu GrattonMathieu Gratton , and Patrick RichardPatrick Richard View All Author Informationhttps://doi.org/10.1097/01.JU.0001008868.74763.2c.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Most patients diagnosed with small renal masses (SRMs) undergo invasive treatment (e.g., nephrectomy or thermal ablation), even though these masses may be benign or have low metastatic potential. Active surveillance has been proposed as an alternative to decrease over-treatment of SRMs. Data from observational studies suggest an increased mortality <1-2% when this approach is compared to invasive treatments. The study seeks to determine patients' values and preferences regarding the management of their SRMs. METHODS: In this Canadian multicenter prospective values and preference study, structured online interviews were conducted with asymptomatic patients who were newly diagnosed with SRMs and who have not yet chosen their treatment with their urologist. During these interviews, patients were first presented with outcomes data and asked their preference in terms of invasive treatment (i.e., nephrectomy vs. surgery). Next, patients were presented with hypothetical scenarios and were asked to choose, using a ping-pong approach, the maximum increase in the probability of death from kidney cancer that they would be willing to accept to decline an invasive treatment and chose active surveillance. RESULTS: Thirty-six participants were interviewed. Of these, 70.3% preferred to be treated by thermal ablation rather than surgery. In addition, the median maximal increase in the probability of death from kidney cancer that they were willing to accept to avoid the negative aspects of an invasive treatment was 0.1% (IQR: 0.1 - 6%). Nevertheless, 25% of patients reported a threshold ≥5%. The majority of patient preferred to be presented with data in the form of 'reduction in the risk of mortality' as opposed to 'increase in the risk of death' or had no preference between both presentation method. Despite the low risk threshold reported by most patients, over 55% of patients opted for AS after meeting with their urologist. CONCLUSIONS: Based on our small multicenter study, when presented with the best available data, the vast majority of patients interviewed preferred thermal ablation to surgery. However, after consultation with their urologist the majority selected active surveillance as their treatment choice. Further studies are needed to elucidate this change reflect a change in patient preference or access issues. Download PPT Source of Funding: KCRNC-KCC-CUASF and CRMUS Research Grants © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e1216 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Clara Diaz More articles by this author Maryam Kandi More articles by this author Philippe Violette More articles by this author Gordon Guyatt More articles by this author Mathieu Gratton More articles by this author Patrick Richard More articles by this author Expand All Advertisement PDF downloadLoading ...

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.174
Threshold uncertainty score0.346

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0150.001

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 head0.046
GPT teacher head0.284
Teacher spread0.238 · 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 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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