Evaluating operational parameters of the careHPV, GeneXpert, AmpFire, and MA-6000 HPV systems for cervical precancer screening: Experience from Battor, Ghana
Bibliographic record
Abstract
In response to calls by the World Health Organization for cervical precancer screening services in low-resource settings to lean toward HPV DNA testing, a number of testing platforms have been made available. This study aimed to evaluate the operational parameters of four HPV testing systems in previous (careHPV) and current (GeneXpert, AmpFire, and MA-6000) use in a secondary healthcare setting in terms of 'appropriateness', ease of use, throughput, and diagnostic yield. This descriptive retrospective cohort analysis included 6056 women who presented to our facility between June 2016 and March 2022 for cervical precancer screening via HPV testing. A large majority of this cohort underwent AmpFire testing (55.8%), followed by careHPV (23.3%), MA-6000 (14.7%), and GeneXpert (6.1%). MA-6000 showed the highest hr-HPV positivity rate of 26.4% (95% CI, 23.6-29.5), followed by AmpFire (17.2%; 95% CI, 15.9-17.5). GeneXpert and careHPV showed similar hr-HPV positivity rates of 14.8% (95% CI, 11.3-18.8) and 14.8% (95% CI, 13.0-16.8), respectively. For the AmpFire and MA-6000 platforms, which utilize similar detection and reporting formats, we found a significant excess detection rate of 9.2% (95% CI, 6.1-12.4; p-value <0.0001) for MA-6000 compared to AmpFire. At the genotype level, MA-6000 also detected significantly higher rates of HPV 16 and other hr-HPV types (both p-values <0.001) than AmpFire; there was no difference in detection for HPV 18. Based on our experiences and preliminary analysis, we believe that the choice of HPV testing platform cannot be accomplished with a one-size-fits-all approach. Factors worth considering are the financial implications of platform acquisition, costs to clients, and throughput when screening programs are not sufficiently large. We describe our successes and challenges with the different platforms which we believe will be helpful to centers in low-income countries as they transition into using HPV DNA testing for cervical precancer screening.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".