Patient-Reported Outcomes in Alberta: Rationale, Scope, and Design of a Database Initiative
Bibliographic record
Abstract
Background: The collection of patient reported outcomes (pros) is a standard of care in many cancer organizations. In Alberta, pros have been integrated into routine clinical practice since 2012. This longitudinal collection of pros provides a wealth of data and a unique research opportunity to improve cancer care. The goal of this pro data initiative is to establish a robust repository of information for ongoing clinical care and research focused on pros. In this paper, we describe the rationale, scope, and design of this initiative. Implementation: The initiative consists of pros and other administrative health data from the province of Alberta. Retrieval of health data from a variety of provincially governed sources will create a platform of information on pros, health outcomes, cancer data, other health conditions, and demographics. The aims of the initiative are to use the data to inform best practices at the point of care; to conduct health services research, particularly clinical epidemiology studies; and to evaluate a variety of pro-related outcomes. Discussion: Because this effort represents our first to integrate routinely collected pros with other administrative health data, a unique and robust data repository will be created. The ability to integrate various types of data will provide a comprehensive mechanism to evaluate a variety of outcomes. Because cancer care in Alberta is governed by a single health care system, the data linkages will include population health and psychosocial cancer data. We anticipate that research related to this initiative will ultimately help to inform more patient-centred care.
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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 | 0.171 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".