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Record W2913735441 · doi:10.2105/ajph.2018.304763

Michael Reese Hospital and the Campaign to Warn the US Public of the Long-Term Health Effects of Ionizing Radiation, 1973–1977

2019· article· en· W2913735441 on OpenAlexfundno aff
Itai Bavli, Shifra Shvarts

Bibliographic record

VenueAmerican Journal of Public Health · 2019
Typearticle
Languageen
FieldMedicine
TopicHistory of Medical Practice
Canadian institutionsnot available
FundersNational Institutes of HealthUniversity of British ColumbiaHebrew University of Jerusalem
KeywordsIonizing radiationPublic healthTerm (time)Environmental healthMedicinePathologyAstronomyPhysics

Abstract

fetched live from OpenAlex

In July 1973, a study at the University of Chicago linked radiation treatment during childhood to a variety of diseases, including thyroid cancer. A few months later, a worker at Michael Reese Hospital in Chicago, Illinois found a registry of 5266 former patients who had been treated with radiation during the 1950s and 1960s. Hospital officials decided to contact these patients and arrange for follow-up medical examinations. Media coverage of the hospital's campaign had a snowball effect that prompted more medical institutions to follow suit, resulting in the National Cancer Institute (NCI) launching a nationwide campaign to warn the public and medical community about the late health effects of ionizing radiation. This study describes how the single action of a hospital in Chicago and the media attention it attracted led to a national campaign to warn those who underwent radiation treatment during childhood.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.014
GPT teacher head0.295
Teacher spread0.281 · 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.

Study designQualitative
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

Citations6
Published2019
Admission routes1
Has abstractyes

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