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Record W2901182980 · doi:10.1017/dmp.2018.113

Medical Spending for the 2001 Anthrax Letter Attacks

2018· article· en· W2901182980 on OpenAlexaff
Nicholas A. Zacchia, Ketra Schmitt

Bibliographic record

VenueDisaster Medicine and Public Health Preparedness · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacillus and Francisella bacterial research
Canadian institutionsConcordia UniversityUniversity of British Columbia
Fundersnot available
KeywordsPreparednessEconomic costPublic healthMedical emergencyMedicineEnvironmental healthOccupational safety and healthBusinessPolitical scienceEconomicsNursing

Abstract

fetched live from OpenAlex

ABSTRACTIntroductionThis paper assesses the total medical costs associated with the US anthrax letter attacks of 2001. This information can be used to inform policies, which may help mitigate the potential economic impacts of similar bioterrorist attacks. METHODS: Journal publications and news reports were reviewed to establish the number of people who were exposed, were potentially exposed, received prophylactics, and became ill. Where available, cost data from the anthrax letter attacks were used. Where data were unavailable, high, low, and best cost estimates were developed from the broader literature to create a cost model and establish economic impacts. RESULTS: Medical spending totaled approximately $177 million. CONCLUSIONS: The largest expenditures stemmed from self-initiated prophylaxis (worried well): people who sought prophylactic treatment without any indication that they had been exposed to anthrax letters. This highlights an area of focus for mitigating the economic impacts of future disasters. (Disaster Med Public Health Preparedness. 2019;13:539-546).

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.101
GPT teacher head0.408
Teacher spread0.307 · 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
GenreEmpirical

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

Citations17
Published2018
Admission routes1
Has abstractyes

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