<p>Methods for Measuring the Time of Transfer from Pediatric to Adult Care for Chronic Conditions Using Administrative Data: A Scoping Review</p>
Bibliographic record
Abstract
PURPOSE: To describe methods used to identify the timing of transfer from pediatric to adult care within health administrative data and to identify the advantages and limitations of each method to guide future research. STUDY DESIGN AND SETTINGS: We conducted a scoping review to identify studies, summarized challenges of identifying the timing of transfer, and proposed methodological approaches for each. RESULTS: Studies use the following approaches to capture individuals who transfer from pediatric to adult care by 1) defining the timing of transfer by the last pediatric and first adult care visit last and 2) defining transfer to adult care based on a specific age. CONCLUSION: There are important limitations of administrative data that must be recognized in designing studies examining the transfer to adult care.
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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 | 0.175 | 0.426 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.040 | 0.044 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".