Evidence map›Paper›PMID 32118122›Full record

ArticleForensic chemistry (Amsterdam, Netherlands)2019

A multi-laboratory investigation of drug background levels.

Edward Sisco, Marcela Najarro

Open access · greenAbstract read
In one paragraph

Article in Forensic chemistry (Amsterdam, Netherlands), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.0field-weighted citation impact, top 29% 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

6 citing papers in PubMed, 10 citations in OpenAlex.

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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 at 1 institution in 1 country.

Edward SiscoNational Institute of Standards and Technology, Gaithersburg, MD 20899, United States.
Marcela NajarroNational Institute of Standards and Technology, Gaithersburg, MD 20899, United States.
National Institute of Standards and Technology · US

Funding

Intramural NIST DOC 9999-NIST
6 · The paper itself

Abstract

Identifying and quantifying the drug background in operational environments such as forensic laboratories is an emerging body of research. Knowing these levels is crucial to addressing issues like occupational exposure risk - due to the emergence of potent novel psychoactive substances and synthetic opioids - and data integrity - due to improvements in instrument sensitivity. The work presented here builds upon a prior study to provide a broader representation of the average drug background levels found on surfaces in forensic laboratories. Over 700 samples from 20 laboratories were collected, extracted, and analyzed quantitatively using LC-MS/MS, and qualitatively using TD-DART-MS. Quantitative analysis by LC-MS/MS included a panel of 18 drugs while the non-targeted qualitative analysis by TD-DART-MS screened for over three hundred drugs and excipients. The study focused primarily on surfaces within the drug unit and evidence receiving area of the laboratories, but also investigated other operational units (crime scene, drug interdiction, latent prints, and toxicology) as well as report writing. Background levels were highest within the drug unit of the laboratory, though detectable (tens of nanograms) levels were observed in nearly all sampled areas. The data from this expanded study plays a critical role in addressing laboratory concerns such as establishing drug identification reporting limits for new instrumentation and establishing new workflow or cleaning protocols while also providing a more comprehensive dataset for general environmental background studies.

Indexed as

BackgroundDrugsLC–MS/MSQuantitationTD-DART-MS

Identifiers

PMID32118122
PMCPMC7047777
OpenAlexW2972595151

What OpenQuestion holds

Textmetadata
LicenceTDM
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.