ICES Report: Increasing Access to Health Administrative Data with ICES Data & Analytic Services
Bibliographic record
Abstract
The Institute for Clinical Evaluative Sciences (ICES) is one of only a few organizations in Ontario permitted to access, link and analyze health administrative data for the purpose of generating evidence to inform decisions in policy and practice. Although ICES is a leading research institute, its access to the data has historically been restricted to scientists with an ICES affiliation. This requirement, designed to meet ICES' data privacy and security obligations, created barriers with respect to the widespread use of Ontario's data assets. In 2014, as part of the government's commitment to the Strategy for Patient-Oriented Research, ICES launched the Data & Analytic Services platform, which is aimed at increasing access to data and analytic services to investigators external to ICES. In making the data widely available to the broader research community, this initiative engages investigators involved in front-line care, stimulates new avenues of research and fosters collaboration that was previously challenging or unfeasible.
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 | 0.115 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".