eOutcomes-H&N: Development of an electronic point-of-care outcomes collection system for head and neck cancer.
Bibliographic record
Abstract
247 Background: Outcomes data increases the ability to understand the impact of cancer treatment and helps ensure that we direct appropriate investments to achieve improvements in quality of care. Cancer Care Ontario (CCO) has developed an outcomes data collection system for patients with head and neck cancer treated with radiotherapy. Methods: Clinicians involved in the care of these patients agreed on 5 key clinical outcomes to collect as a part of this initiative: local failure, regional failure, distant metastasis, second primaries, and treatment toxicity. Based on the Princess Margaret Hospital Head and Neck Cancer Anthology of Outcomes process, CCO has developed a similar system to collect outcomes data at the point-of-care on a provincial scale. The population-based provincial system aims to provide practice efficiencies and allow for evolution of the system in response to user feedback and expansion to other clinical areas. Results: The system includes a secure web application and iPad mobile application to facilitate the collection and management of outcomes data for head and neck cancer patients treated with radiotherapy in Ontario. Cancer centres upload patient clinic schedule details (including patient identifiers and appointment dates) prior to clinics. A list of patient summaries, including corresponding diagnosis, radiotherapy information and previously recorded outcomes is then generated. In clinic, physicians use these lists to capture outcomes noted at the point-of-care. Data captured are then transferred in real time to a secure central database. Following initial testing, the application was piloted to assess usability, system reliability, and overall satisfaction. Initial results show high physician satisfaction and no documented issues with system availability. Feedback has identified potential improvements and will inform future modifications. Conclusions: The identification of potential variation in recurrence, toxicity and survival data will inform the areas in which quality improvement initiatives or additional investments may be needed. The intent is to leverage project learnings for future outcomes initiatives in other diseases and treatment modalities.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.014 |
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".