Inter‐Laboratory Comparison of <scp>qPCR</scp> Assays for <scp><i>Piscirickettsia salmonis</i></scp> in Atlantic Salmon (<scp><i>Salmo salar</i></scp> L.) in 11 Chilean Laboratories
Bibliographic record
Abstract
ABSTRACT Real‐time PCR (qPCR) testing is an essential component of early detection surveillance systems for Piscirickettsia salmonis infection in Atlantic salmon farms in Chile. Currently, all 11 laboratories in the authorised diagnostic laboratory network use assays based on published protocols. Compared with other P. salmonis qPCR assays, these assays have the advantage of targeting two different genes, that is, the 16S ribosomal gene and the internal transcribed spacer (ITS), potentially allowing for higher diagnostic accuracy. However, variation and lack of harmonisation of qPCR testing systems (e.g., primers/probe, RNA/DNA as target, extraction methods, etc.) may contribute to among‐laboratory variation in qPCR results and an increased frequency of false‐negative and false‐positive results. The purpose of the ring trial reported herein was to compare qPCR results from 11 laboratories in Chile routinely testing Atlantic salmon for P. salmonis as part of a national control program. The panel of 14 samples included duplicates of three concentrations of P. salmonis in a homogenised head kidney, LF89 and EM90 bacteria and two negative controls (blank and a suspension of Flavobacterium psychrophilum ). The sample order was randomised across labs, samples were tested blinded and analysed without knowledge of the source lab. Of the laboratories, 8 (72.7%) had at least one incorrect result out of 14 tested samples. Low‐concentration samples ( C t of about 30) were more often incorrectly classified by reverse transcription‐qPCR (RT‐qPCR) (3/6 labs) than by qPCR (0/5). Six (54.5%) labs had at least one false‐positive result indicating that cross‐contamination was likely during sample processing. Affected laboratories are advised to conduct internal investigations to confirm the causes of false‐positive results and recommendations for design and implementation of future ring trials are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".