Toward A Population-Based Approach to End-Of-Life Care Surveillance in Canada: Initial Efforts and Lessons
Bibliographic record
Abstract
This paper describes a project undertaken by the Hospice Palliative End-of-Life Care Surveillance Team Network--one of four Cancer Surveillance and Epidemiology Networks established by the Canadian Partnership Against Cancer in 2009 to create information products that can be used to inform cancer control. The project was designed to improve the quality and use of existing electronic patient databases in its member organizations. The project's intent was to better understand terminally ill cancer patients in their final year of life, with noncancer as comparison. The network created an early design for a Web-based end-of-life care surveillance system prototype. Using a flagging process, anonymized data sets on cancer/ noncancer palliative patients and those who died in 2008-2009 were extracted and analyzed. The Australian palliative approach was adapted as the conceptual model based on the data sets available. Common data elements were defined then mapped to local data sets to create a common data set. Information products were created as online reports. Throughout the project, members were engaged in knowledge translation. Overall, the project was well received by network members. There are still major data-quality and linkage issues that require further work.
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 | 0.044 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".