Healthcare Professional Clinical Actions following Nutrigenomics Testing in Practice
Bibliographic record
Abstract
BACKGROUND: Clinical demand for nutrigenomics testing (NGT) is increasing, underscoring the importance of assessing healthcare professional (HCP) competence and clinical actions with NGT in practice. While previous studies have explored HCP perceptions of NGT, no study has examined real HCP experiences with NGT in practice. OBJECTIVE: The objective of this study was to evaluate the clinical experience of providing NGT among early adopter HCPs who have used NGT in their practice. We hypothesized that HCP clinical actions after NGT would differ according to HCP personal experience undergoing genetic testing (GT) as well as years in practice. DESIGN: An online survey questionnaire was administered to HCPs (n = 70) who have provided NGT in practice. χ2 tests, tests for trend, and logistic regression were used to compare HCP characteristics with post-NGT outcomes. RESULTS: HCPs with fewest years in practice (<5 years) comprised the lowest proportion of respondents (16%). Most HCPs reported good understanding of NGT results and 92% made genetic-based dietary recommendations to patients following NGT. HCP personal use of GT increased significantly with increasing years in practice (<5 years: 36%, 5-10 years: 53%, 11-20 years: 70%, and >20 years: 85%, p trend = 0.003). Requesting patient bloodwork because of NGT results increased significantly with HCP years in practice when HCPs with <5 years in practice were not considered (5-10 years: 19%, 11-20 years: 28%, and >20 years: 60%, p trend = 0.010). A near significant difference was observed where a greater proportion of HCPs who had personally undergone GT reported requesting patient bloodwork (personal use: 46% vs. no personal use: 23%, p-χ2 = 0.066). CONCLUSION: Early HCP adopters of NGT utilize the test results to provide genetic-based dietary recommendations to patients. Clinical action after NGT currently appears to be driven by HCP years in practice, but HCP personal use of GT may also be a factor.
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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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".