Bibliographic record
Abstract
Recent years have witnessed the development of new record linkage technologies that are increasingly being used for data integration in various application settings. The authors’ objective in this article is to provide a review of recent developments in medical record linkage and their implications in healthcare research and public health policies. In particular, the authors assess the key advantages and possible limitations of record linkage techniques and technologies in various health care scenarios where different pieces of patient records are collected and managed by different agencies. First, the authors provide a brief overview of deterministic, probabilistic, and unsupervised record linkage techniques and their advantages and limitations. Then, the authors describe current probablistic record linkage software and their functionalities, and present specific cases where probabilistic linkage has been successfully used to enhance decision-making in healthcare delivery as well as in healthcare-related public policy making. Finally, the authors outline some of the critical issues and challenges of integrating medical records across distributed databases, including technical considerations as well as concerns about patient privacy and confidentiality.
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 | 0.084 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.029 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".