Quality assessment of linked Canadian clinical administrative hospital and vital statistics death data
Bibliographic record
Abstract
IntroductionThree Canadian clinical-administrative hospital databases were linked to the Canadian Vital Statistics Death Database (CVSD) to provide information about patients who died following discharge from hospital as well as supplementary information about patients that died in-hospital. Quality was assessed using a guided approach and through feedback from initial users.
 Objectives and ApproachThe linked datasets were created to develop and validate health care indicators and performance measures and perform outcome analyses. It is therefore imperative to evaluate the data’s fitness for use. Quality was assessed by calculating coverage of deaths for all linked contributors, creating a profile of the linked dataset and analyzing issues that were identified by users. These analyses were guided by an existing Data Source Assessment Tool, which provides a set of criteria that allow for assessment across five dimensions of quality, thus allowing for appropriate determination of a given set of data’s fitness for use.
 ResultsDeterministic linkage of the datasets resulted in linkage rates that ranged from 66.9% to 90.9% depending on the dataset or data year. Linkage rates also varied by Canadian jurisdictions and patient cohort. Variables had good data availability with rates of 95% or higher. Initial users identified a significant number of duplicate records that were flagged to and corrected by the data supplier. 1.4\% of acute hospital deaths had discrepancies in the death date captured in the two linked sources; the vast majority had a difference of only one day. A user group and issue tracking process were created to share information about the linked data and guarantee that issues are triaged to the appropriate party and allow for timely follow up with the data supplier.
 Conclusion/ImplicationsDocumentation provided by the data supplier was vital to understanding the linkage methodology and its impact on linkage rates. A guided data assessment ensured that strengths and limitations were identified and shared to support appropriate use. Feedback to the data supplier is supporting ongoing improvements to the linkage methodology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.008 |
| 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 | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".