Challenges with the use of Xpert HPV as a screening tool for oral HPV among people living with HIV (PLHIV): experiences from Pune, India
Bibliographic record
Abstract
BACKGROUND: People living with HIV (PLHIV) are at higher risk for human papillomavirus (HPV)-related oropharyngeal cancers compared to the general population. Xpert HPV test is a polymerase chain reaction (PCR) assay capable of rapid HPV detection. Performing the assay requires minimal intervention by laboratory personnel. Its use could improve oropharyngeal cancer screening among PLHIV living in low-and middle-income countries (LMICs) with limited diagnostic capacities. However, Xpert HPV performance for oral samples has not been evaluated. Here, we describe our experience with Xpert HPV and compare its results with traditional PCR, for oral samples. METHODS: Oral samples from 429 PLHIV receiving care at a tertiary care hospital affiliated antiretroviral therapy center in Pune, India were used. Samples were collected either after a 30s oral rinse and gargle (n = 335) or in combination with cytobrush scraping of the oral mucosa (n = 91). Unsuccessful tests were those that generated an invalid or error result on Xpert HPV. Successful tests were those that generated a positive or negative result. Kappa statistic was used to compare concordance between Xpert HPV and traditional real-time PCR results. RESULTS: There were 29.8% (n = 127) unsuccessful tests, of which 78.7% (n = 100) were invalid and 21.3% (n = 27) were error results. Adding cytobrush scraping to oral rinse as a collection procedure did not significantly reduce the proportion of unsuccessful tests (p = 0.9). For successful tests, HPV positivity on Xpert was 0.3% (n = 1/299). Kappa statistic was 0.11, indicating poor agreement between Xpert HPV and traditional PCR results. CONCLUSIONS: Presently, Xpert HPV appears to have limited use for oral HPV detection among PLHIV using oral samples. More research to improve the diagnostic capabilities of Xpert HPV for oral samples among PLHIV is needed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".