Bibliographic record
Abstract
P roviding appropriate chemotherapy to patients with cancer may be expensive, and the drugs used are potentially toxic.To ensure appropriate use of health care resources, an assessment of the benefits of cancer chemotherapy is necessary and provides valuable information to both the public and funding agencies.The Manitoba Oncology Drug Utilization and Clinical Outcomes (MODUCO) Program carries out such assessments by evaluating the therapeutic and economic outcomes of chemotherapy regimens.An initiative of CancerCare Manitoba's Provincial Oncology Drug Program (PODP), the MODUCO Program is supported by the CancerCare Manitoba/Winnipeg Regional Health Authority Oncology Pharmacotherapeutic Subcommittee.Encompassing the principles of evidence-based decision-making, stewardship, transparency, and equal access, the PODP was established in 2006 to provide the infrastructure for the effective use and financial management of oncology drugs in Manitoba.The MODUCO Health Outcomes Analyst is a joint position funded by the PODP and the Epidemiology and Cancer Registry of CancerCare Manitoba.This position is currently filled by pharmacist Kimi Guilbert, who plays a key and unique role in the planning, organization, collection, and analysis of information relevant to determining the benefits and costs of chemotherapeutic regimens.
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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.102 | 0.031 |
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".