Screening Donors of Solid Organs for West Nile Virus: First, Do No Harm!
Bibliographic record
Abstract
H ealth Canada has recently published guidelines that pro- mote testing donors of tissues and perfusable organs for West Nile virus (WNV) using nucleic acid amplification testing (NAT) (such as polymerase chain reaction) (1).It is my belief that the rationale underlying these guidelines is fundamentally flawed and that these guidelines will harm (or already have harmed) patients.My primary concerns relate to polymerase chain reaction testing of donors of perfusable organs.There is no doubt that transplanted organs can transmit WNV and that infected recipients may experience particularly severe outcomes (2).The Health Canada guidelines suggest that all donors should have NAT testing performed before transplantation.These guidelines do not suggest that such testing be considered in the context of disease activity (either local or seasonal epidemiology).For instance, under these guidelines, a donor who has not left Manitoba for months should be tested for WNV at midnight on New Year's Eve.The failure to consider the pretest probability of active infection, when combined with a lack of knowledge of the performance characteristics of available tests (which are licensed and have not been formally evaluated for this indication), is a recipe for disaster (eg, discarding life-saving organs).
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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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".