Administrative health databases for addressing emerging issues in adults with CHD: a systematic review
Bibliographic record
Abstract
The need for population-based studies of adults with CHD has motivated the growing use of secondary analyses of administrative health data in a variety of jurisdictions worldwide. We aimed at systematically reviewing all studies using administrative health data sources for adult CHD research from 2006 to 2016. Using PubMed and Embase (1 January, 2006 to 1 January, 2016), we identified 2217 abstracts, from which 59 studies were included in this review. These comprised 12 different data sources from six countries. Of these, 55% originated in the United States of America, 28% in Canada, and 17% in Europe and Asia. No study was published before 2007, after which the number of publications grew exponentially. In all, 41% of the studies were cross-sectional and 25% were retrospective cohort studies with a wide variation in the availability of patient-level compared with hospitalisation-level episodes of care; 58% of studies from eight different data sources linked administrative data at a patient level; and 37% of studies reported validation procedures. Assessing resource utilisation and temporal trends of relevant epidemiological and outcome end points were the most reported objectives. The median impact factor of publication journals was 4.04, with an interquartile range of 3.15, 7.44. Although not designed for research purposes, administrative health databases have become powerful data sources for studying adult CHD populations because of their large sample sizes, comprehensive records, and long observation periods, providing a useful tool to further develop quality of care improvement programmes. Data linkage with electronic records will become important in obtaining more granular life-long adult CHD data. The health services nature of the data optimises the impact on policy and public health.
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 | 0.025 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.051 | 0.042 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".