629. Malaria Screening Turnaround Time: Comparative Multicenter Analysis of Negative and Positive Results and Impacts of Rapid Diagnostic Test Implementation and Laboratory Centralization
Bibliographic record
Abstract
Abstract Background Screening tests for malaria diagnosis should be reported rapidly because of its potential for rapid progression to severe disease and relatively high mortality. Guidelines recommend a high level of expertise for microscopy diagnosis and a laboratory test result turnaround time (TAT) ≤ 2 hours. Meeting these requirements can be challenging in the era of global laboratory centralization. Methods Multicenter retrospective cohort study comparing negative (2012-2021) and positive (2014-2023) malaria screening results within and between 2 central laboratories/tertiary academic centers in Quebec, Canada, in addition to data from one center’s primary care affiliated sites. Results 1296 negative malaria tests were analyzed for complete analytical cycle TAT: Center A n=225 (central lab n=100; affiliated sites n=125), and Center B n=1120. After rapid diagnostic test (RDT) implementation, all sites have significantly reduced delays and ≤ 2h TAT compliance is > 90% for central labs (median: A 38 min, B 53min). But affiliated sites’ compliance is low at 19.6% (median 6h12) with delays mostly attributable during transit between labs (median TAT from central lab sample intake is 25 min). Comparison of positive malaria result TATs to negative result TATs for both server lab centers will be presented, with a trend to longer delays to smear results for negative RDTs (median 4h13) vs positive RDTs (3h33). Detailed results from central lab – affiliated sites model will be presented with several practical recommendations. In preliminary results, no cases were diagnosed solely with thick smear, suggesting little added sensitivity in our center when used in addition to RDT + thin smear. Conclusion Quality and timely malaria diagnostic services are crucial for urgent clinical decisions given the potential for high morbidity and mortality. TATs are necessary lab quality management indicators during laboratory centralization and data for each component of malaria testing identifies weak links in the diagnostic chain. Such studies provide opportunities for quality and efficiency improvement, and for restructuring testing algorithms accordingly. Disclosures All Authors: No reported disclosures
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".