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Record W4302566761

Tuberculosis control activities before and after Hurricane Sandy--northeast and mid-Atlantic states, 2012.

2013· article· en· W4302566761 on OpenAlexaboutno aff

Bibliographic record

VenuePubMed · 2013
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsnot available
Fundersnot available
KeywordsStormAtlantic hurricanePreparednessTuberculosisMedicineHurricane katrinaNatural disasterHealth careGeographyEnvironmental healthMeteorologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

On October 29, 2012, Hurricane Sandy struck the U.S. northeast and mid-Atlantic seaboard; the effects of the storm extended to southeastern and midwestern states and to eastern Canada. At the time, 1,899 residents in the most affected areas were undergoing treatment for tuberculosis (TB) disease or infection. To ascertain the operational abilities of state and local TB programs during and after the storm and to determine whether lessons learned from a previous hurricane were effective in ensuring continuity of TB patient care, CDC interviewed staff members at all of the affected state and city TB control programs, including those in areas with power outages and flooded streets, tunnels, and subway lines. The interviews determined that continuity of care for TB patients in programs affected by Hurricane Sandy was better preserved than it had been during and after Hurricane Katrina in August 2005. This improvement might be attributed to 1) preparedness measures learned from Hurricane Katrina (e.g., preparing line lists of patients, providing patients with as-needed medications, and making back-up copies of patient records in advance of the storm) and 2) less widespread displacement of persons after Hurricane Sandy than occurred after Hurricane Katrina. Maintaining readiness among clinicians and TB control programs to respond to natural disasters remains essential to protecting public health and preserving TB patients' continuity of care.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.194
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.267
Teacher spread0.254 · 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 teacher head, 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

Citations2
Published2013
Admission routes1
Has abstractyes

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