Evidence map›Paper›PMID 36185582›Full record

ArticleEuropean urology open science2022

Modeling Disease Trajectories for Castration-resistant Prostate Cancer Using Nationwide Population-based Data.

Eugenio Ventimiglia, Anna Bill-Axelson, Jan Adolfsson, Markus Aly, Martin Eklund, Marcus Westerberg, Pär Stattin, Hans Garmo

Abstract read
In one paragraph

Article in European urology open science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Eugenio VentimigliaDepartment of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Anna Bill-AxelsonDepartment of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Jan AdolfssonDepartment of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden.
Markus AlyDepartment of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
Martin EklundDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Marcus WesterbergDepartment of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Pär StattinDepartment of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Hans GarmoDepartment of Surgical Sciences, Uppsala University, Uppsala, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Little is known about disease trajectories for men with castration-resistant prostate cancer (CRPC). Objective: To create a state transition model that estimates time spent in the CRPC state and its outcomes. Design setting and participants: The model was generated using population-based prostate-specific antigen data from 40% of the Swedish male population, which were linked to nationwide population-based databases. We compared the observed and predicted cumulative incidence of transitions to and from the CRPC state. Outcome measurements and statistical analysis: We measured time spent in the CRPC state and the proportion of men who died of prostate cancer during follow-up by CRPC risk category. Results and limitations: Time spent in the CRPC state varied from 1.1 yr for the highest risk category to 3.9 yr for the lowest risk category. The proportion of men who died from prostate cancer within 10 yr ranged from 93% for the highest risk category to 54% for the lowest. There was good agreement between the model estimates and observed data. Conclusions: There is large variation in the time spent in the CRPC state, varying from 1 yr to 4 yr according to risk category. Patient summary: It is possible to accurately estimate the disease trajectory and duration for men with castration-resistant prostate cancer.

Indexed as

Castration-resistant prostate cancerState transition modelSurvival

Identifiers

PMID36185582
PMCPMC9520495

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.