A positive history of varicella (chickenpox) has high positive predictive value
Bibliographic record
Abstract
When somebody reports a past history of varicella, does it mean he/she already had varicella (and therefore immune)? Similarly, when he/she says no, does it really mean she never had varicella (and therefore susceptible to this infection)? These questions occur not infrequently in clinical practice and have assumed increasing relevance with the availability of varicella immunisation. In a systematic review of published cross-sectional studies comparing history of varicella and varicella-zoster virus serology, Holmes reported these findings: positive predictive values (PPV) 95-98.5%, negative predictive values (NPV) 6-44%. The results show that a positive history of varicella is almost always correct but a negative history of varicella is inaccurate (most of them actually had varicella). It is noteworthy that all the 12 studies evaluated by Holmes were conducted in the developed countries (USA, Canada, Australia, Ireland), we need to bear in mind that the problem of late seroconversion in tropical countries (i.e. more adults who have never had varicella) and lack of accuracy of past history in patients with low literacy. (copied from article)
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".