Reporting quality of quantitative polymerase chain reaction (qPCR) methods in scientific publications
Bibliographic record
Abstract
Abstract Reproducibility is a significant concern in scientific research and complex methods like quantitative polymerase chain reaction (qPCR) demand stringent reporting standards to ensure that the methods are reproducible, data are sound, and conclusions are trustworthy. Although the MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) guidelines were introduced in 2009 to improve qPCR reporting, a 2013 study identified ongoing deficiencies that hinder reproducibility. To further investigate the transparency and completeness of qPCR reporting, we systematically assessed articles published in the top 20 journals in genetics and heredity (n=186) and plant sciences (n=246) that used qPCR. Our analysis revealed frequent omissions and inadequate specification of critical information necessary for evaluating and replicating qPCR experiments. RNA integrity, along with assessment methods and instruments used to assess it, are seldom reported. Although primer sequences are often disclosed, names and accession numbers of housekeeping genes are frequently omitted. Additionally, essential details about RNA extraction, RNA-to-cDNA conversion, and qPCR, such as kit names, catalog numbers, and reagent information, are often missing. Our findings underscore the urgent need for improved reporting practices in qPCR experiments, emphasizing quality controls, detailed descriptions of reagents and materials, and greater analytical transparency. Addressing these reporting deficiencies is crucial for enhancing the reproducibility and evaluating the trustworthiness of qPCR research. Potential solutions include encouraging authors to cite protocols published in online repositories, providing reporting templates, or developing automated tools to check reporting compliance.
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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 | 0.402 | 0.771 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.034 | 0.037 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".