Evidence map›Paper›PMID 39678690›Full record

ArticleAesthetic surgery journal. Open forum2024

Utilization of Artificial Intelligence to Improve Equitable Healthcare Access for Breast Implant Patients.

Louisa B Ragsdale, Aurora M Kareh, Rohun Gupta, Peter K Firouzbakht, Christina M Plikaitis, Katherine A Rodby

Abstract read
In one paragraph

Article in Aesthetic surgery journal. Open forum, 2024. 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.

Aurora M Kareh
Peter K Firouzbakht
Christina M Plikaitis
Katherine A Rodby

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recently, mandated FDA patient decision checklists were developed with the goal of improving the informed decision-making process for patients considering breast implants. However, these checklists are written at reading levels far higher than recommended by the National Institutes of Health and the American Medical Association. This study aims to improve the accessibility, and therefore, the utility of the mandated FDA patient literature for the average breast implant patient using the assistance of artificial intelligence (AI). Patient decision checklists were obtained from the 3 most utilized breast implant manufacturers in the United States-Allergan, Mentor, and Sientra. A novel patient decision checklist was synthesized by AI, written at the sixth grade reading level, using these checklists as source material. The AI-assisted checklist was edited by plastic surgeons for both formatting and content. The overall readability of Allergan, Mentor, and Sientra patient checklists correlated with the college reading level. These documents were of a statistically significantly higher reading level than the AI-assisted checklist, which was written at the recommended sixth grade level. Text composition analysis similarly demonstrated substantial differences between the AI-assisted and FDA-mandated literature. The currently mandated breast implant patient checklists are written at a college reading level and are inaccessible to the average patient. The authors propose a new patient decision checklist, generated with the assistance of AI, to improve healthcare access within plastic surgery. This simplified material can be used as an adjunct to the current checklists to improve shared decision making.

Identifiers

PMID39678690
PMCPMC11646116

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.