Frances Oldham Kelsey, the FDA, and the Battle against Thalidomide
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".