Bibliographic record
Abstract
A search for prostate cancer information on the Web will turn up more news than you can possibly use. To help people through this confusing maze, Dalhousie University‚s Department of Urology has created a new site (www .caprostate .com) that includes everything from a detailed description of the disease to sets of top-quality related links and definitions. For newly diagnosed patients, it‚s an ideal place to visit.This site also goes a step further than most by offering a fully interactive online program to help patients identify treatment options according to the stage of their tumour. Clicking into the site‚s Treatment Facilitator page pulls up a simple form-based tool (see accompanying illustration). The page asks for a medical history that focuses on prostate cancer issues. It then performs an analysis and presents the patient with a range of individualized treatment options. ”Narrowing down potential treatment options is extremely valuable for patients,” says Dr. David Bell, an associate professor in the Department of Urology and the project leader. ”Caprostate .com helps explain why some treatments are not an option for a particular patient and why other options may be beneficial. This is one of the first Internet programs that goes beyond passive education and actually facilitates treatment decision-making.” The Treatment Facilitator presents options based on current clinical research and existing literature. It is careful to point out these are only options and that actual clinical decisions must be made with the help of the patient‚s physician. As Bell says, the idea is to provide patients with an understanding of the disease so they can begin meaningful discussions with their doctors. In other words, the Treatment Facilitator is designed to help patients make informed decisions about the difficult path they must follow. —
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.134 | 0.044 |
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".