Development of a multiplex polymerase chain reaction for detection and typing of major human herpesviruses in cerebrospinal fluid
Bibliographic record
Abstract
Infections of the central nervous system (CNS) represent a difficult diagnostic problem for both clinicians and microbiologists. In particular, the Herpesviridae family plays a central etiological role in CNS viral infections. These diseases have acquired growing importance in the past few years owing to the increasing number of immunocompromised patients and the availability of new antiviral drugs. Prompt detection and diagnosis of CNS viral infections are critical because most infections are treatable, while a delayed recognition may lead to life-threatening conditions or severe sequelae. The traditional methods for detection of herpesviruses in CNS infections exhibit several drawbacks, whereas the polymerase chain reaction (PCR) on cerebrospinal fluid has revolutionized the neurovirology and is becoming an essential part of the diagnostic work-up of patients with suspected CNS viral infections. A sensitive multiplex PCR method was developed for the simultaneous detection of 6 human herpesviruses (human cytomegalovirus, herpes simplex virus 1, herpes simplex virus 2, Epstein-Barr virus, varicella-zoster virus, and human herpesvirus 6) with the aim of simplifying detection and reducing time and costs. The accuracy, reproducibility, specificity, and sensitivity of these assays were established.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".