Non-invasive prenatal testing (NIPT): a call for change in reporting practices
Bibliographic record
Abstract
The use of non-invasive prenatal testing (NIPT) technology has revolutionized the practice of prenatal screening. The assay’s validity and reliability have been demonstrated in both low- and high-risk pregnancies. Despite its excellent screening parameters, its reliability is often overestimated due to confusing and incorrect terminology that appears in private NIPT reports. Herein, we provide a brief explanation of the potential implications at two different levels: patient and provider. We conclude with a call to redesign the way information is presented on NIPT reports to avoid stressing patients, enhance transparency in clinical counselling, and perhaps most critically, to prevent medical decisions that may not be warranted solely based on the NIPT results.
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 | 0.206 | 0.385 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.023 | 0.036 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".