Evidence map›Paper›PMID 35377775›Full record

ArticleThe Journal of urology2022

Variation in Communication of Competing Risks of Mortality in Prostate Cancer Treatment Consultations.

Timothy J Daskivich, Rebecca Gale, Michael Luu, Aurash Naser-Tavakolian, Abhi Venkataramana, Dmitry Khodyakov, Jennifer T Anger, Edwin Posadas, Howard Sandler, Brennan Spiegel and 1 more

Abstract read
In one paragraph

Article in The Journal of urology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Observational
  3. Article
  4. Scoring Physician Risk Communication in Prostate Cancer Using Large Language Models.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026
    Article
  5. Article
  6. Scoring Physician Risk Communication in Prostate Cancer Using Large Language Models.medRxiv : the preprint server for health sciences · 2025
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Use of Persuasive Language in Communication of Risk during Prostate Cancer Treatment Consultations.Medical decision making : an international journal of the Society for Medical Decision Making · 2024
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Timothy J DaskivichDepartment of Surgery, Division of Urology, Cedars-Sinai Medical Center, Los Angeles, California.ORCID 0000-0002-7540-3272
Rebecca GaleCedars-Sinai Center for Outcomes Research and Education (CS-CORE), Cedars-Sinai Medical Center, Los Angeles, California.
Michael LuuDepartment of Biostatistics, Cedars-Sinai Medical Center, Los Angeles, California.
Aurash Naser-TavakolianDepartment of Surgery, Division of Urology, Cedars-Sinai Medical Center, Los Angeles, California.
Abhi VenkataramanaDepartment of Urology, University of Southern California, Los Angeles, California.
Dmitry KhodyakovRAND Institute, Santa Monica, California.
Jennifer T AngerDepartment of Urology, University of California, San Diego, San Diego, California.
Edwin PosadasDepartment of Medicine, Division of Medical Oncology, Cedars-Sinai Medical Center, Los Angeles, California.
Howard SandlerDepartment of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, California.
Brennan SpiegelCedars-Sinai Center for Outcomes Research and Education (CS-CORE), Cedars-Sinai Medical Center, Los Angeles, California.
Stephen J FreedlandDepartment of Surgery, Division of Urology, Cedars-Sinai Medical Center, Los Angeles, California.

Funding

A Patient-Centered Approach to Integration of Life Expectancy into Treatment Decision Making for Patients with Genitourinary MalignancyK08CA230155 · NCI · CEDARS-SINAI MEDICAL CENTER · PI DASKIVICH, TIMOTHY · 2018 to 2022
$909k
NCI NIH HHS K08 CA230155
6 · The paper itself

Abstract

purposeMen with prostate cancer prefer patient-specific, quantitative assessments of longevity in shared decision making. We sought to characterize how physicians communicate the 3 components of competing risks-life expectancy (LE), cancer prognosis and treatment-related survival benefit-in treatment consultations. MATERIALS AND

methodsConversation related to LE, cancer prognosis and treatment-related survival benefit was identified in transcripts from treatment consultations of 42 men with low- and intermediate-risk disease across 10 multidisciplinary providers. Consensus of qualitative coding by multiple reviewers noted the most detailed mode of communication used to describe each throughout the consultation.

resultsPhysicians frequently failed to provide patient-specific, quantitative estimates of LE and cancer mortality. LE was omitted in 17% of consultations, expressed as a generalization (eg "long"/"short") in 17%, rough number of years in 31%, probability of mortality/survival at an arbitrary timepoint in 17% and in only 19% as a specific number of years. Cancer mortality was omitted in 24% of consultations, expressed as a generalization in 7%, years of expected life in 2%, probability at no/arbitrary timepoint in 40% and in only 26% as the probability at LE. Treatment-related survival benefit was often omitted; cancer mortality was reported without treatment in 38%, with treatment in 10% and in only 29% both with and without treatment. Physicians achieved "trifecta"-1) quantifying probability of cancer mortality 2) with and without treatment 3) at the patient's LE-in only 14% of consultations.

conclusionsPhysicians often fail to adequately quantify competing risks. We recommend the "trifecta" approach, reporting 1) probability of cancer mortality 2) with and without treatment 3) at the patient's LE.

Indexed as

ProstateProstatic NeoplasmsCommunicationHumansLife ExpectancyMaleReferral and Consultationagingcomorbiditylife expectancyprostatic neoplasms, survival

Identifiers

PMID35377775
PMCPMC11070128

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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.