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Frances Oldham Kelsey, the FDA, and the Battle against Thalidomide

2024· book· en· W4396498558 on OpenAlexaffabout
Cheryl Krasnick Warsh

Bibliographic record

Venuenot available
Typebook
Languageen
FieldMedicine
TopicHistorical Medical Research and Treatments
Canadian institutionsVancouver Island University
Fundersnot available
KeywordsBattleMedicineFood and drug administrationAlternative medicineThalidomideLawGerontologyFamily medicinePolitical scienceHistoryPharmacologyAncient historyInternal medicine

Abstract

fetched live from OpenAlex

Abstract In the 1960s, Dr. Frances Oldham Kelsey of the US Food and Drug Administration (FDA) became one of the most celebrated women in America when she prevented a deadly sedative from entering the US market. A Canadian-born pharmacologist and physician, Kelsey saved countless Americans from the devastating side effects of thalidomide, a drug routinely given to pregnant women to prevent morning sickness. As a result of Kelsey’s efforts, thalidomide was never sold in the United States. The incident led Congress to pass the 1962 Kefauver-Harris Amendments to the Food, Drug and Cosmetic Act, which fundamentally changed drug regulation in America. One of a small minority of women to earn an advanced degree in science in the 1930s, Kelsey faced challenges that resonate with women scientists to this day. Revered by the public as a “good mother of science,” she went on to act as a formidable gatekeeper against other suspect drugs, such as diethylstilbestrol (DES) and laetrile. Based upon FDA archival records, private family papers, and interviews with family and colleagues, this biography brings to light the efforts and legacy of a pioneering woman of science whose contributions remain influential.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.015
GPT teacher head0.286
Teacher spread0.272 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2024
Admission routes2
Has abstractyes

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