Evidence map›Paper›PMID 38090372›Full record

ArticleCancer innovation2023

Clustering of prostate cancer healthcare pathways in the French National Healthcare database.

Roméo Baulain, Jérémy Jové, Dunia Sakr, Marine Gross-Goupil, Magali Rouyer, Marius Puel, Patrick Blin, Cécile Droz-Perroteau, Régis Lassalle, Nicolas H Thurin

Open access · diamondAbstract read
In one paragraph

Article in Cancer innovation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.1field-weighted citation impact, top 13% of its field
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

2 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. 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

10 authors at 2 institutions in 1 country.

Roméo BaulainÉcole nationale de la statistique et de l'administration économique Paris (ENSAE) Institut Polytechnique Paris Palaiseau France.
Jérémy JovéUniv. Bordeaux, INSERM CIC-P 1401, Bordeaux PharmacoEpi Bordeaux France.
Dunia SakrUniv. Bordeaux, INSERM CIC-P 1401, Bordeaux PharmacoEpi Bordeaux France.
Marine Gross-GoupilMedical Oncology, Hôpital Saint André CHU de Bordeaux Bordeaux France.ORCID 0000-0002-3176-4076
Magali RouyerUniv. Bordeaux, INSERM CIC-P 1401, Bordeaux PharmacoEpi Bordeaux France.ORCID 0000-0002-2560-4412
Marius PuelUniv. Bordeaux, INSERM CIC-P 1401, Bordeaux PharmacoEpi Bordeaux France.
Patrick BlinUniv. Bordeaux, INSERM CIC-P 1401, Bordeaux PharmacoEpi Bordeaux France.ORCID 0000-0003-4005-7928
Cécile Droz-PerroteauUniv. Bordeaux, INSERM CIC-P 1401, Bordeaux PharmacoEpi Bordeaux France.ORCID 0000-0002-7697-1167
Régis LassalleUniv. Bordeaux, INSERM CIC-P 1401, Bordeaux PharmacoEpi Bordeaux France.ORCID 0000-0001-6726-6215
Nicolas H ThurinUniv. Bordeaux, INSERM CIC-P 1401, Bordeaux PharmacoEpi Bordeaux France.ORCID 0000-0003-3589-0819
Université de Bordeaux · FRCentre Hospitalier Universitaire de Bordeaux · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Healthcare pathways of patients with prostate cancer are heterogeneous and complex to apprehend using traditional descriptive statistics. Clustering and visualization methods can enhance their characterization. Methods: Patients with prostate cancer in 2014 were identified in the French National Healthcare database ( Results: The repartition of macro-variables values-surveillance, local treatment, androgenic deprivation, and advanced treatment-appeared to be consistent with prostate cancer status. Two to five clusters of healthcare pathways were observed in each of the different cohorts, corresponding for most of them to relevant clinical patterns, although some heterogeneity remained. For instance, clustering allowed to distinguish patients undergoing active surveillance, or treated according to cancer progression risk in HSPC, and patients receiving treatment for potentially curative or palliative purposes in mHSPC and mCRPC. Conclusion: Visualization methods combined with a clustering approach enabled the identification of clinically relevant patterns of prostate cancer management. Characterization of these care pathways is an essential element for the comprehension and the robust assessment of healthcare technology effectiveness.

Indexed as

clusteringhealthcare pathwaymachine learningprostate cancerSNDS

Identifiers

PMID38090372
PMCPMC10686138
OpenAlexW4317395168

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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Registered trials

None linked

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.