Development of a remote monitoring program for melanoma/skin oncology patients at Princess Margaret Cancer Centre.
Bibliographic record
Abstract
e18630 Background: The Melanoma/Skin Oncology (MSO) clinic supports > 200 patients on active treatment at any given time. In September 2018, the MSO clinic implemented a nurse-led proactive management program (IMBRASE) to support the monitoring of adverse events (AEs) in patients receiving immunotherapy (IO). Nurses conducted telephone calls at a pre-determined frequency to identify those who required immediate in-person assessment. Despite an initial 40% drop in emergency department visits in the first few months of implementation, insufficient human resources and lack of prioritization of assessments impacted the clinic’s ability to sustain this program. We sought to overcome this limitation by employing digital health technology to monitor and triage on-treatment patients for assessment (eIMBRASE program). Methods: MSO team worked alongside an internal innovation team (Cancer Digital Intelligence) to develop a remote assessment program with an algorithm able to prioritize patients by combining real-time biometrics and automatically scheduled electronic patient-reported outcomes (ePROs) data of individuals undergoing targeted-therapy and IO. This new digital tool will be accessible via smartphone/computer. It will enable patients to send their data to the clinical team and establish priorities based on patients’ complexity of symptoms. Results: eIMBRASE algorithm requires four data points to be displayed for the clinical team: overall priority, cumulative score (CS), biometric alert, and no response alert. The first two points are based on ePROs, for which we established a set of symptoms of interest for each treatment modality. Each symptom can be categorized in 1 of the 6 priority levels. Each priority is associated with a number of complexity points on a logarithmic scale from 0 - 400. After a questionnaire is submitted, a CS is generated for clinicians to determine which patient may be the most complex and deserve priority assessment. Regarding the third data point, oxygen saturation (SpO2) and temperature (T) measures are collected throughout the day and patients are flagged according to the following predefined thresholds: T - red alert > 38.5C, orange 38.1-38.4C, yellow 37.5-38C; and SpO2 - red alert £93%. Patients under the highest risk of AEs, for instance those on anti-CTLA4 + anti-PD1, are required to complete ePROs daily during the first 12 weeks. Patients under low-risk therapies are required to complete ePROs 1x/week. The tool triggers priority-oriented timeframe for medical contact. To minimize the influence of poor electronic literacy, patients will have their apps set up at clinic before treatment initiation. Conclusions: eIMBRASE development presents an alternative way to use digital health tools to potentially improve quality of care for MSO patients. A pilot study will be conducted to assess its feasibility, the impact in patients’ outcomes and satisfaction.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".