Out‐of‐pocket and private pay in clinical genetic testing: A scoping review
Bibliographic record
Abstract
Full coverage of the cost of clinical genetic testing is not always available through public or private insurance programs, or a public healthcare system. Consequently, some patients may be faced with the decision of whether to finance testing out-of-pocket (OOP), meet OOP expenses required by their insurer, or not proceed with testing. A scoping review was conducted to identify literature associated with patient OOP and private pay in clinical genetic testing. Seven databases (EMBASE, MEDLINE, CINAHL, PsychINFO, PAIS, the Cochrane Database of Systematic Reviews, and the JBI Evidence-Based Practice database) were searched, resulting in 83 unique publications included in the review. The presented evidence includes a descriptive analysis, followed by a narrative account of the extracted data. Results were divided into four groups according to clinical indication: (1) hereditary breast and ovarian cancer, (2) other hereditary cancers, (3) prenatal testing, (4) other clinical indications. The majority of studies focused on hereditary cancer and prenatal genetic testing. Overall trends indicated that OOP costs have fallen and payer coverage has improved, but OOP expenses continue to present a barrier to patients who do not qualify for full coverage.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".