Bibliographic record
Abstract
When Sarah Boston wakes up one morning to find a lump in her neck, she is alarmed. A veterinary surgical oncologist by profession, she wastes no time in diagnosing it as cancer. That turns out to be the easy part. In Lucky Dog: How Being a Veterinarian Saved My Life, Sarah Boston relates the story of her odyssey through our health care system. Her illness gives her a unique perspective as she divides her time between being the doctor and being the patient. Boston describes the frustrations that she faces in convincing her doctors that she has cancer and the ensuing delays in getting treatment. Two surgeries, numerous medications, and a series of radioactive iodine treatments later, Boston is dismayed by the differences between our human system and the one for our pets. Granted that her clients are paying for the service, she feels that veterinarians spend more time with their patients, explain things better, and provide more options than our own doctors. She writes: “We need to do better. We need to care more. We need to advocate more.” By comparing the quality of health care provided to people with that for animals, this book takes a thought-provoking look at how our socialized health care system functions. In this vein, Boston also discusses the controversy surrounding doctor-assisted suicide for humans as opposed to the accepted practice of humanely “putting down” our beloved pets, allowing them a dignified, peaceful death. The book is informative, entertaining, and inspiring. Boston faces her own illness with courage. Although the subject is cancer, the book is not dark or depressing. Boston’s style is conversational and honest and spiced throughout with her quirky and irrepressible sense of humour. She intersperses the story of her cancer treatment with those of her brave and stoical canine patients whom she writes are “blissfully ignorant of the anguish their owners are going through.” Dr. Boston received her Doctor of Veterinary Medicine from the University of Saskatchewan and her Doctor of Veterinary Science from the University of Guelph. She is currently an associate professor at the University of Florida’s veterinary school in Gainesville, Florida and President of the Veterinary Society of Surgical Oncology.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".