Bibliographic record
Abstract
According to the Canadian Cancer Society, half of Ontario’s population will be diagnosed with cancer in their lifetime. Many patients being assessed for cancer however become overwhelmed when having to manage information overload, many appointments with different instructions and locations, and recommendations on how to improve their lifestyle. This causes much anxiety and uncertainty among patients. Some cancer assessment clinics offer some guidance in the form of paper-based patient navigators, which provide much reliable information to patients but are limited in terms of dynamic updates to appointments, opportunities for sharing knowledge between healthcare providers and patients, and of patients supporting each other. This thesis proposes a new web-based, mobile, and user-friendly virtual patient navigator application framework named Care Ami, which incorporates the information found in an existing paper-based navigator along with the new features such as remote updates to personal care paths and calendars, personalized navigation guidance, sharing of symptoms/medications information, and peer group support. Unlike existing solutions, Care Ami is configurable to support multiple types of diseases (e.g., lung cancer and breast cancer). This application is evaluated through testing and the usage of heuristic evaluation guidelines related to usability, and a comparison with related work highlights its many benefits.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".