Diagnostic accuracy of dried blood spots for serology of vaccine-preventable diseases: a systematic review
Bibliographic record
Abstract
Venous serum and plasma are optimal specimens for serological testing but may be logistically infeasible. Dried blood spots (DBS) are a feasible alternative, provided results are adequately sensitive and specific. We aimed to assess the diagnostic accuracy of DBS to measure IgG and IgM antibodies for vaccine-preventable diseases and compare test validity of DBS with venous blood. In October 2020, we searched seven databases for peer-reviewed studies assessing the diagnostic accuracy of DBS specimens compared with serum in detecting antibodies to VPDs in humans. We extracted data and assessed risk of bias in all included studies. We calculated sensitivity and specificity with 95% confidence intervals for each index-reference test comparison. We narratively synthesized the identified evidence on diagnostic accuracy and blood collection and processing methods for DBS. Studies on measles and rubella IgG and IgM were the most frequently identified and reported generally high sensitivity and specificity. Lack of standardization in collection, storage, and testing methods limited systematic comparison across studies. Our findings indicate a need for additional validation studies on the diagnostic accuracy of DBS to expand their use in serological surveillance. We recommend practical considerations to improve standardized reporting for DBS validation studies.
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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 | 0.017 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".