Optimal Strategies for Determining the Duration of Washout Period in the Context of Identifying Chronic Disease Onset Cases Based on Administrative Data: a Systematic Review
Bibliographic record
Abstract
Background When using administrative data, the establishment of clear and appropriate duration of chronic disease washout period is the basis for correctly identifying the point of onset of chronic disease in patients with recurrent visits and identifying new cases. Objective To review the methods for determining the duration of washout period by a systematic review of literature, in order to provide ideas for Chinese researchers to confirm the duration of washout period and correctly identify new cases with subsequent use of administrative data to identify new cases of chronic diseases. Methods In October 2021, PubMed, Web of Science, EmBase, CNKI, CQVIP and Wanfang Knowledge Service Platform were systematically searched for literature on the use of administrative data to explore the incidence and prevalence of chronic diseases from inception to 2022-10-01. Two researchers independently screened literatures and extracted relevant information, evaluated the quality of literature by using the Standards for Reporting Qualitative Research (SRQR) scale, and summarized the determination method of the duration of washout period. Results A total of 26 papers were included, and the SRQR scores of the included literature were all≥15, indicating good methodological quality. The data used in the literature were mainly from Canada, the United States, Australia and other countries (regions) with complete and abundant administrative data, and focused on a variety of chronic diseases, including diabetes, tumor, schizophrenia and other chronic diseases. The study pointed out that setting an appropriate washout period duration is the basis for accurate identification of onset cases. The methods used to determine the duration of washout period in literature were mainly divided into three major categories, including direct restriction method, consistency test method and retrograde survival function method. Among them, the most commonly used method was direct restriction method, while the retrograde survival function method had a relatively low usage rate. Conclusion Direct restriction method, consistency test method and retrograde survival function method all have corresponding advantages and limitations. The selection, judgment criteria and stability of the method need to be further explored.
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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 | 0.092 | 0.274 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.017 |
| Bibliometrics | 0.029 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".