Evidence map›Paper›PMID 40152856›Full record

ArticleEnvironmental health perspectives2025

Categorizing Concentration Confidence: A Framework for Reporting Concentration Measures from Mass Spectrometry-Based Assays.

Lauren M Petrick, David Achaintre, Amith Maroli, Julio Landero, Priyanthi S Dessanayake, Susan L Teitelbaum, Mary S Wolff, Manish Arora, Robert O Wright, Syam S Andra

Abstract read
In one paragraph

Article in Environmental health perspectives, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Lauren M PetrickDepartment of Environmental Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.ORCID 0000-0003-3493-7419
David AchaintreDepartment of Environmental Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Amith MaroliDepartment of Environmental Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Julio LanderoDepartment of Environmental Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Priyanthi S DessanayakeDepartment of Environmental Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Susan L TeitelbaumDepartment of Environmental Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Mary S WolffDepartment of Environmental Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Manish AroraDepartment of Environmental Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Robert O WrightDepartment of Environmental Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Syam S AndraDepartment of Environmental Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

Funding

Statistical Services and Methods Development ResourceU2CES026555 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2015 to 2025
$26.4M
Untargeted Analysis ResourceU2CES026561 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI WRIGHT, ROBERT O · 2015 to 2025
$24.5M
The Mount Sinai Transdisciplinary Center on Early Environmental ExposuresP30ES023515 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Chris Gennings · 2014 to 2026
$21.3M
Stress, Lead, Iron Deficiency and NeurodevelopmentR01ES013744 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Robert O Wright · 2007 to 2026
$13.4M
Untargeted Analysis ResourceU2CES030859 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI ARORA, MANISH · 2019 to 2025
$8.2M
Early Warning Systems for Childhood and Adult DisordersR35ES030435 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Manish Arora · 2020 to 2026
$6.7M
BREAST CANCER AND THE ENVIRONMENT ON LONG ISLANDU01CA066572 · NCI · UNIVERSITY OF NORTH CAROLINA CHAPEL HILL · PI GAMMON, MARILIE D · 1995 to 2004
$1.7M
NCI NIH HHS U01 CA066572NIEHS NIH HHS P30 ES023515NIEHS NIH HHS R01 ES013744NIEHS NIH HHS R35 ES030435NIEHS NIH HHS U2C ES026555NIEHS NIH HHS U2C ES026561NIEHS NIH HHS U2C ES030859
6 · The paper itself

Abstract

backgroundInnovation in mass spectrometry-based methods to both quantify and perform discovery has blurred the lines between targeted and untargeted assays of biospecimens. Continuous data-concentrations or intensity values generated from both methods-can be used in statistical analysis to determine associations with health outcomes, but concentration values are needed to compare measurements from one study to another to inform policy making decisions and to develop clinically relevant thresholds. As a single solution for discovery and quantitation, new hybrid-type assays derive concentration values for chemicals or metabolites but with varying degrees of uncertainty that may be greater than traditional quantitative assays. There is no current single standard to guide reporting bioassay concentrations or their uncertainty in concentration values from hybrid assays. Even when measures are robust, obtained with high scientific rigor, and provide valuable data toward risk assessment, unknown uncertainty can lead to bias in interpretation of reported data or omission of reported data that does not meet the strict criteria for absolute quantitation.

objectiveThe objective of this commentary is to articulate a scheme that enables investigators across bioanalytical fields to easily report analyte measurement assurance on the same scale from quantitative, untargeted, or hybrid assays that include a range of concentration confidences. DISCUSSION: We propose a simple scheme to report concentrations for targeted and untargeted analytes. Level 1 is a confirmed concentration following established tolerances in a fully quantitative assay while level 5 is a tentative intensity from a typical untargeted assay. This framework enables easy communication of uncertainty in concentration measurements to aid cross-validation, meta-analysis, and extrapolation across studies. It will facilitate interpretation while supporting analytical advancement and allow clear and concise measurement reporting across a broad range of confidences. https://doi.org/10.1289/EHP15465.

Indexed as

Biological AssayEnvironmental MonitoringMass SpectrometryHumansRisk AssessmentUncertainty

Identifiers

PMID40152856
PMCPMC12068507

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.