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Record W2508648698 · doi:10.1097/jom.0000000000000775

Introduction to Department of Defense Research on Burn Pits, Biomarkers, and Health Outcomes Related to Deployment in Iraq and Afghanistan

2016· article· en· W2508648698 on OpenAlexaff
Timothy M. Mallon, MAJ Patricia Rohrbeck, MAJ Kevin M. Haines, Dean P. Jones, Mark J. Utell, Philip K. Hopke, Richard P. Phipps, Douglas I. Walker, Thomas H. Thatcher, Collynn F. Woeller, Coleen P. Baird, Harvey B. Pollard, Clifton L. Dalgard, Joel C. Gaydos

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsDiscovery Air (Canada)
FundersNational Institute of Environmental Health SciencesNational Institute of General Medical Sciences
KeywordsEnvironmental healthMedicineSoftware deploymentCohort studyBiomarkerCohortPathologyEngineering

Abstract

fetched live from OpenAlex

OBJECTIVE: This paper provides an overview of our study that was designed to assess the health impact of environmental exposures to open pit burning in deployed troops. METHODS: The rationale for the study and the structure of the research plan was laid out. An overview of each article in the supplement was provided. The cohort of deployed Service members was assessed for airborne exposure, relevant biomarkers, and health outcomes following deployment to Balad, Iraq, and/or Bagram, Afghanistan. RESULTS: Polycyclic aromatic hydrocarbon (PAH) exposures were elevated, and serum biomarkers were statistically different postdeployment. Associations were noted between PAHs and dioxins and microRNAs. Some health outcomes were evident in deployers compared with nondeployers. CONCLUSIONS: Future research will examine the associations between demographic variables, smoking status, biomarker levels, and related health outcomes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.041
GPT teacher head0.351
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations29
Published2016
Admission routes1
Has abstractyes

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Same venueJournal of Occupational and Environmental MedicineSame topicToxic Organic Pollutants ImpactFrench-language works237,207