Bibliographic record
Abstract
Dr Hamm was the first oncologist to receive an academic appointment in Windsor and has since spearheaded the development of Windsor oncology into an academic program. She completed her fellowship training in hematology and stem cell transplant in Detroit and has since returned to her hometown of Windsor. We had the opportunity to talk with Dr Hamm about the impact of social medicine on cancer prognosis, chemotherapy hesitancy, critical care for migrant workers and coping with death. “We’re really grateful to have had this talk with someone working in oncology because I’m now realizing just how much social medicine plays an important and visible role, maybe especially in oncology, because of cancer’s chronic nature, and the social aspect of people’s lives shapes so much of the supports a person can access and rely on.” – Retage “Social medicine plays a unique role in each specialty of medicine but it’s really interesting hearing about social considerations from the oncology perspective where the biology aspect is so complex that the social factors are often overlooked but play at least, if not greater, of a role on outcomes and patient experience.” – Victoria
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.021 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 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".