Evidence map›Paper›PMID 39398691›Full record

ReviewCureus2024

Black Robes and White Coats: Daubert Standard and Medical and Legal Considerations for Medical Expert Witnesses.

Joseph Pergolizzi, Jo Ann K LeQuang

Abstract readReview
In one paragraph

Review in Cureus, 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

2 authors.

Joseph PergolizziPain Medicine, NEMA Research, Inc., Naples, USA.
Jo Ann K LeQuangScientific Communications, NEMA Research, Inc., Naples, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Expert testimony can play a pivotal role in a legal case involving a medical issue, but it is crucial that this testimony be scientifically sound. In the United States, the Frye Standard demanded that such expert medical testimony be "generally accepted," but it has been superseded by the more demanding Daubert Standard. Under Daubert, judges became "gatekeepers" as to what scientific material was admissible in court and what might be dismissed as junk science. While a vast improvement over Frye, the Daubert Standard still faces its own problems. Judges usually lack scientific and medical expertise and sometimes have to do their own medical research to be able to evaluate the experts and their testimony. Expert witnesses can be subjected to an adversarial Daubert challenge, even during trial, to establish their credentials and the evidence they are reporting. About half of all judges have had no formal education in handling scientific evidence, but 91% felt comfortable in their gatekeeper role. Considerations for expert witnesses include training or coaching, familiarity with the entire case and courtroom procedures, and an ability to not just know the evidence but to be able to communicate it effectively to a lay audience (jury). Some countries have pioneered a concurrent testimony approach in complex cases, whereby experts argue the evidence of the case under oath before a judge in advance of the trial to determine where there are points of agreement and where the main areas of contention reside. This process, colorfully called "hot tubbing," is aimed at a more conciliatory approach to reaching compromises. Nevertheless, bias still can result even in hot-tubbing cases, and more research is needed to better understand the nature of jury bias and how it can affect jury decisions in complex medical cases.

Indexed as

daubert standardfrye standardlawsuitslegal medical casemedical expert witness

Identifiers

PMID39398691
PMCPMC11471272

What OpenQuestion holds

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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.