Bibliographic record
Abstract
William Coley, a young surgeon at New York Memorial Hospital, was traumatized by the loss of his first patient to bone cancer in 1891. He was unable to save this young patient and she succumbed to her Sarcoma within 3 months of surgery. He searched the hospital archive to learn more about Sarcoma and discovered the case of a patient with a large sarcoma who had undergone five unsuccessful surgeries over a 3 year period. This case had been determined to be hopeless. After the last of these operations, the patient became very ill from an erysipelas infection. Coley was astonished to read that after the fever broke and the patient had recovered, the tumour had vanished. Seven years later, the patient was still alive and well. Coley concluded that whatever had caused the fever must also have destroyed the cancer. Coley searched for and found this patient still in excellent health. Coley reasoned that if a chance infection could make tumours vanish, then a purposefully induced infection could do the same. The hypothesis was tested by infecting his next 10 patients with Streptococcus pyogenes to cause Erysipelas. Some of the patients were difficult to infect, some died, and some had a strong reaction and their disease regressed. Coley switched to deactivated S. pyogenes to avoid the mortality observed with the live strain. Afterxperimentation with various formulations, a combination of S pyogenes and Serratia marcescens was decided upon and became known as Coley’s Toxin. The preferred method of delivery was injection of the toxin directly into the primary tumour or metastases in increasing doses to avoid immune tolerance. Fever response in the patient was essential to imitate a naturally occurring infection and the body’s natural response. Though Coley met with success, this therapy was abandoned as chemotherapy became more popular. Hoption Cann SA, Gunn HD, van Netten JP, van Netten C. Dr William Coley and tumour regression: a place in history or in the future. Post Graduate Medical Journal 2003; 79:672-680. Hobohm U. Fever and Cancer in Perspective. Cancer Immunology & Immunotherapy 2001; 50:391-396. Grange JM, Standord JL, Stanford CA. Campbell De Morgan’s ‘Observations on cancer’, and their relevance today. Journal of the Royal Society of Medicine 2002 (June); 95:296-299.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".