Evidence map›Paper›PMID 38222330›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2023

Caregivers Attitude Detection From Clinical Notes.

Gaetano Manzo, Leo Anthony Celi, Yasmeen Shabazz, Rory Mulcahey, Lorenzo Jaime Flores, Dina Demner-Fushman

Abstract read
PubMed Full text at PMC
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2023. 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

6 authors.

Gaetano ManzoComputational Health Research Branch, National Library of Medicine, Bethesda, Maryland, USA.
Leo Anthony CeliMassachusetts Institute of Technology (MIT), Harvard Medical School, and the Beth Israel Deaconess Medical Center.
Yasmeen ShabazzMassachusetts Institute of Technology (MIT), Harvard Medical School, and the Beth Israel Deaconess Medical Center.
Rory MulcaheyComputational Health Research Branch, National Library of Medicine, Bethesda, Maryland, USA.
Lorenzo Jaime FloresMassachusetts Institute of Technology (MIT), Harvard Medical School, and the Beth Israel Deaconess Medical Center.
Dina Demner-FushmanComputational Health Research Branch, National Library of Medicine, Bethesda, Maryland, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Caregivers' attitudes impact healthcare quality and disparities. Clinical notes contain highly specialized and ambiguous language that requires extensive domain knowledge to understand, and using negative language does not necessarily imply a negative attitude. This study discusses the challenge of detecting caregivers' attitudes from their clinical notes. To address these challenges, we annotate MIMIC clinical notes and train state-of-the-art language models from the Hugging Face platform. The study focuses on the Neonatal Intensive Care Unit and evaluates models in zero-shot, few-shot, and fully-trained scenarios. Among the chosen models,

Indexed as

Burnout, ProfessionalCaregiversAttitudeHumansInfant, NewbornQuality of Health Care

Identifiers

PMID38222330
PMCPMC10785866

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.