From Paralytic Poison to Medicinal Marvel: Curare Advances Anesthesia
Bibliographic record
Abstract
After extracting curare from vines like Chondrodendron tomentosum (left), South American natives stored the tarry toxin in pots (right) and slathered it on arrow tips to slay enemies and prey. Scientists would call the active compound d-tubocurarine for the bamboo tubes (right) that held the arrows. In the 1730s, French mathematician Charles Marie de La Condamine became the first to study curare during an Andean expedition to prove Newton's view of Earth as an oblate spheroid-rounder at the Equator than the poles. At the time, he attributed the death of a curare-injected hen to respiratory muscle paralysis. In a famed 1814 experiment, English naturalist Charles Waterton and colleagues used artificial ventilation to keep a curarized donkey alive. But it was not until 1938, when North American explorer Richard Gill imported 12 kg of Ecuadorian curare to treat his own muscle spasms, that the arrow poison would enter anesthetic practice. After E. R. Squibb & Sons acquired Gill's supply and purified it to make Intocostrin (right), former anesthesiologist and Squibb advisor Lewis H. Wright supplied the drug to Canadian anesthesiologist Harold Griffith. The latter soon published in Anesthesiology his success with Intocostrin for rapid muscle relaxation in 25 lightly anesthetized patients (1942; 3:418-20). And so began a revolution, in which the drawbacks of deep anesthesia-cardiac depression, explosion risk, severe nausea, and prolonged emergence-could finally be mitigated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".