Evidence map›Paper›PMID 36427022›Full record

ArticleThe oncologist2023

The Use of Natural Language Processing to Assess Social Support in Patients With Advanced Cancer.

Sunil Bhatt, P Connor Johnson, Netana H Markovitz, Tamryn Gray, Ryan D Nipp, Nneka Ufere, Julia Rice, Matthew J Reynolds, Mitchell W Lavoie, Madison A Clay and 2 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The oncologist, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.8field-weighted citation impact, top 25% 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

5 citing papers in PubMed, 8 citations in OpenAlex.

  1. Trial
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  5. Review
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

12 authors at 3 institutions in 1 country.

Sunil BhattDepartment of Medicine, Division of Hematology & Oncology, Massachusetts General Hospital, Boston, MA, USA.
P Connor JohnsonDepartment of Medicine, Division of Hematology & Oncology, Massachusetts General Hospital, Boston, MA, USA.ORCID 0000-0002-3943-6608
Netana H MarkovitzDepartment of Medicine, Division of Hematology & Oncology, Massachusetts General Hospital, Boston, MA, USA.
Tamryn GrayHarvard Medical School, Boston, MA, USA.ORCID 0000-0001-8075-9735
Ryan D NippDepartment of Medicine, Division of Hematology & Oncology, Massachusetts General Hospital, Boston, MA, USA.
Nneka UfereHarvard Medical School, Boston, MA, USA.
Julia RiceDepartment of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
Matthew J ReynoldsDepartment of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
Mitchell W LavoieDepartment of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
Madison A ClayDepartment of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
Charlotta LindvallHarvard Medical School, Boston, MA, USA.
Areej El-JawahriDepartment of Medicine, Division of Hematology & Oncology, Massachusetts General Hospital, Boston, MA, USA.ORCID 0000-0002-3556-1495
Massachusetts General Hospital · USHarvard University · USBrigham and Women's Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundData examining associations among social support, survival, and healthcare utilization are lacking in patients with advanced cancer.

methodsWe conducted a cross-sectional secondary analysis using data from a prospective longitudinal cohort study of 966 hospitalized patients with advanced cancer at Massachusetts General Hospital from 2014 through 2017. We used NLP to identify extent of patients' social support (limited versus adequate as defined by NLP-aided review of the Electronic Health Record (EHR)). Two independent coders achieved a Kappa of 0.90 (95% CI: 0.84-1.00) using NLP. Using multivariable regression models, we examined associations of social support with: 1) OS; 2) death or readmission within 90 days of hospital discharge; 3) time to readmission within 90 days; and 4) hospital length of stay (LOS).

resultsPatients' median age was 65 (range: 21-92) years, and a plurality had gastrointestinal (GI) cancer (34.3%) followed by lung cancer (19.5%). 6.2% (60/966) of patients had limited social support. In multivariable analyses, limited social support was not significantly associated with OS (HR = 1.13, P = 0.390), death or readmission (OR = 1.18, P = 0.578), time to readmission (HR = 0.92, P = 0.698), or LOS (β = -0.22, P = 0.726). We identified a potential interaction suggesting cancer type (GI cancer versus other) may be an effect modifier of the relationship between social support and OS (interaction term P = 0.053). In separate unadjusted analyses, limited social support was associated with lower OS (HR = 2.10, P = 0.008) in patients with GI cancer but not other cancer types (HR = 1.00, P = 0.991).

conclusionWe used NLP to assess the extent of social support in patients with advanced cancer. We did not identify significant associations of social support with OS or healthcare utilization but found cancer type may be an effect modifier of the relationship between social support and OS. These findings underscore the potential utility of NLP for evaluating social support in patients with advanced cancer.

Indexed as

Natural Language ProcessingNeoplasmsAgedCross-Sectional StudiesHumansLongitudinal StudiesProspective Studieshospitalizationnatural language processingsocial supportsurvival analysis

Identifiers

PMID36427022
PMCPMC9907037
OpenAlexW4310055018

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Textmetadata
LicenceCC BY
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Registered trials

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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.