MétaCan
Menu
Back to cohort
Record W2501667462 · doi:10.1002/ajim.22631

FDNY and 9/11: Clinical services and health outcomes in World Trade Center‐exposed firefighters and EMS workers from 2001 to 2016

2016· review· en· W2501667462 on OpenAlexaboutno aff
Jennifer Yip, Mayris P. Webber, Rachel Zeig‐Owens, Madeline Vossbrinck, Ankura Singh, Kerry Kelly, David J. Prezant

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2016
Typereview
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsnot available
FundersMemorial Sloan-Kettering Cancer Center
KeywordsMedicineWorld trade centerWorkforceOccupational safety and healthMental healthQuarter (Canadian coin)Environmental healthMedical emergencyEmergency medicineGerontologyFamily medicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: After the World Trade Center (WTC) attacks on September 11, 2001, the Fire Department of the City of New York (FDNY) instituted a WTC medical monitoring and treatment program and established a data center to document health outcomes in the WTC-exposed workforce of ∼16,000 firefighters and EMS workers. METHODS: FDNY schedules routine monitoring exams every 12-18 months and physical and mental health treatment appointments, as required. RESULTS: FDNY research studies have consistently found that early arrival to work and/or prolonged work at the WTC-site increased the risks for adverse physical and mental health outcomes. To date, a substantial proportion has been diagnosed with obstructive airways disease, chronic rhinosinusitis, and gastroesophageal reflux disease; a quarter has two or more of these conditions. CONCLUSIONS: While much has been learned, the entire spectrum and trajectory of WTC-related disorders and their mechanisms of onset and persistence remain to be fully described. Am. J. Ind. Med. 59:695-708, 2016. © 2016 Wiley Periodicals, Inc.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.334
GPT teacher head0.550
Teacher spread0.215 · 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 designObservational
Domainnot available
GenreReview

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

Citations55
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Industrial MedicineSame topicOccupational Health and PerformanceFrench-language works237,207