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Influential factors impacting treatment (tx) decision making (TDM) and decision regret (DR) in patients (pts) with localized or locally advanced prostate cancer (LPC/LAPC).

2024· article· en· W4391303614 on OpenAlexaboutno aff
Benjamin A. Gartrell, Angaja Phalguni, Suneel Mundle, Sharon McCarthy, Sabine Brookman‐May, Francesco De Solda, Ruhee Jain, Guillaume Ploussard, Boris Hadaschik

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRegretProstate cancerProstatectomyFamily medicineGynecologyInternal medicineCancer

Abstract

fetched live from OpenAlex

294 Background: Pts newly diagnosed with LPC/LAPC must contemplate many factors to make tx decisions that may later lead to regret. We conducted literature reviews exploring factors associated with pt TDM and DR. Methods: Databases (Ovid Medline, Ovid Embase, Cochrane Library), select congress proceedings and gray literature were searched (12 Sept 2022). Publications (pubs) on pt TDM and tx DR in LPC/LAPC were included following systematic literature review methods and selected based on the following criteria: 2012 onward, ≥100 pts, journal article, and quantitative data. Study quality was assessed by risk of bias tools (NICE STA guidance; Newcastle-Ottawa). Influential factors were those with p<0.05; for TDM, factors described as “a decision driver,” “associated,” “influential,” or “significant” were also included. Key factors were determined under consideration of number of studies, consistency of evidence, and study quality. Results: 75 pubs (68 studies) reported TDM (12 countries; 68% North America [NA] 25% Europe [E]). Pt TDM participation was reported in 34 pubs; overall, pts preferred an active/shared role. Of 39 influential TDM factors, those related to baseline demographics, health and disease, external factors (doctor recommendation most common), and tx goals/attributes were key (Table). 49 pubs reported DR (12 countries; 59% NA, 29% E). DR was assessed at 2 weeks to 6 years post-tx. Not tx specific DR (23 studies), was felt by 8-48% of pts. Regret associated with specific tx (18 studies) was felt by 7-43% (active surveillance), 12-57% (radical prostatectomy), 1-49% (radiotherapy), 28-49% (androgen deprivation therapy), and 21-47% (combination therapy) of pts. Of 42 significant DR factors, tx toxicity (sexual/urinary/bowel dysfunction), pt role in TDM, and making an informed tx decision were key (Table). Conclusions: DR is common in pts with LPC/LAPC. To help pts navigate factors influencing TDM and limit risk of DR, a shared consensual TDM approach between pts, caregivers, and healthcare professionals is needed. [Table: see text]

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)0.021
metaresearch head score (Gemma)0.120
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.120
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.393
GPT teacher head0.573
Teacher spread0.180 · 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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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