Below the OPSpecs chart, enter the four uncertainty components for the selected analyte/level. u(repeat) and u(repro) auto-populate from this analyte's own run history — within-day SD pooled across days for repeatability, and the SD of daily means for reproducibility — but only once there's enough structure to split them (at least 2 distinct days, each with ≥2 runs). u(cal) and u(ref) always need to be entered by hand, from a calibrator certificate or reference-method documentation, because the app has no way to know those values on its own.

Formula: u_c = √(u_repeat² + u_repro² + u_cal² + u_ref²), expanded uncertainty U = 2·u_c (k=2, ~95% coverage under the standard normal approximation used by GUM). If TEa% is set above, U is automatically checked against it as a pass/fail. This is the standard GUM combination rule applied to your numbers — it is exact arithmetic, not a model, and its quality is only as good as the inputs you provide.

1. Enter the analyte name (e.g. "Glucose", "HbA1c") and the result your lab obtained for that PT survey sample.
2. Fill in the Peer Group Statistics — these come from your PT provider's report: peer mean, peer SD, and number of participating labs (N).
3. Enter the TEa % (Total Allowable Error) for your analyte — use RCPA or EQA scheme limits.
4. Add a Lot / Survey number and date for record-keeping.
5. Click ▶ Calculate PT Performance to see your z-score, SDI, and acceptability verdict.
6. Click + Add Current to History to track your z-score trend over multiple surveys.

1. Label your Reference Method (e.g. LC-MS/MS, reference lab) and your Test Method (e.g. immunoassay, point-of-care device).
2. Paste paired measurements into the data box — one pair per line, separated by a tab or comma. Left column = reference, right column = test value.
3. Set λ (lambda): use 1.0 when both methods have similar imprecision (orthogonal Deming). If the reference method is much more precise, use a smaller λ.
4. Enter the TEa % to judge whether the measured bias is clinically acceptable.
5. Click ▶ Run Analysis to generate regression statistics, scatter plot, and Bland-Altman limits of agreement.

1. Select your Analyte from the dropdown or type a name (e.g. "Glucose", "TSH").
2. Enter your laboratory's current CV% (imprecision) — this is found on your IQC chart or sigma calculator.
3. Enter your Bias% — the percentage difference between your method mean and the assigned target value.
4. Set the TEa% (Total Allowable Error) for this analyte. Use your EQA scheme limits or RCPA ALP criteria.
5. Click ▶ Generate Peer Comparison to see your ranked position in the simulated peer group, performance tier, and distribution charts.

1. Select a Target Analyte from Biochemistry, Immunoassay, Urine Chemistry, CSF/Fluids, or Special Tests, and a QC Level (L1/L2/L3).
2. Choose a Fault Scenario: Baseline (in-control), Systematic Step Shift, Progressive Linear Trend, Imprecision (variance doubled), or Intermittent Outliers (3% rate).
3. Choose a Magnitude/Rate: ±1.5 SD (Moderate) up to ±4.0 SD (Extreme).
4. Click ▶ Inject Fault & Run Simulation. The simulator generates 100 synthetic QC observations — the first 50 runs are normal baseline operation, the final 50 contain the injected fault.
5. Review the Levey-Jennings Monitor to see exactly when and how each method (Westgard, EWMA, Isolation Forest, One-Class SVM) flags the fault.
6. Use the Interactive QC & AI Quiz in the sidebar to test understanding of the concepts just demonstrated.

1. Review each algorithm card — the ROC/AUC figures are real (from the manuscript); other qualitative descriptions are illustrative.
2. Examine the ROC Comparison chart for real AUC values matching Table 3 of the accompanying manuscript.
3. Review the Feature Weight Reference to understand what each heuristic formula weights, and why (illustrative — see Figure 6 of the manuscript for real SHAP-based feature importance).
4. The Reproducibility table's confidence intervals are also illustrative, except the Sigma Metric Calc row, which is genuinely exact (deterministic formula).
5. Use Export Validation Report / Export Manuscript Package for a written summary of the real formulas/citations this app uses.

• For publication or accreditation submissions: do not cite any AUC, false-alarm rate, or detection-delay figure from this page — none of them came from an executed study. If you need real comparative figures, generate them yourself with the Training Lab simulator and report what it actually computes, or cite the published literature directly.
• For training/teaching: fine to use as a rough illustration of how detection methods typically trade off sensitivity, speed, and false-alarm rate — just be explicit with learners that the specific numbers are illustrative examples, not measured results.
• What's real on this page: the underlying Westgard, EWMA, and CUSUM math genuinely runs on your own data elsewhere in the app (Dashboard, Charts, Westgard Inspector) and is appropriate to describe accurately in a manuscript Methods section — see the Export Manuscript Package for accurate wording.

1. Enter an analyte name, total QC runs, target mean, and CV%.
2. Optionally enter a systematic bias% and TEa%.
3. Under Fault Injection, choose a fault type and magnitude.
4. Set the run number where the fault should begin.
5. Click Generate Dataset to create and preview the data.
6. Review the chart, sigma classification, and Westgard analysis.
7. Click Export CSV to save the dataset, or Inject into ClinQC to use it live in the app.

• All data generated is fully synthetic — produced by random number generation, no real patient data, QC results, or instrument data is involved.
• Safe for publication: describe generated datasets as "computer-simulated synthetic QC data generated using Gaussian noise modelling with fault injection" — a well-accepted method in laboratory medicine methodology papers.
• Safe for training: ideal for teaching staff how QC faults appear visually on Levey-Jennings charts and how sigma metrics respond to different error types.
• Limitation: synthetic data cannot capture all instrument- or reagent-specific variation. Intended for algorithm demonstration and education, not as a substitute for real method validation data.

1. Scroll through each numbered section to find the method you need.
2. Copy the equation and citation directly into your manuscript's Methods section.
3. Each section identifies the governing standard (ISO, CLSI, Westgard, RCPATH).
4. Use Export Methods PDF to generate a self-contained PDF appendix ready to submit as supplementary material or attach to an accreditation submission.
5. For ISO 15189 submissions, reference specific section numbers in your QMS documentation.

• This page contains no AI-generated claims or synthetic results. Every equation is drawn from established peer-reviewed standards (ISO, CLSI, Westgard) and can be cited directly.
• For journal submission: appropriate for the Methods section. Cite the referenced standards directly (e.g. CLSI C24-Ed4, ISO 15189:2022) rather than citing this software.
• For accreditation (ISO 15189): the exported PDF is suitable as a supporting document demonstrating the mathematical basis of your QC procedures.
• For training: an ideal reference handout — equations, grading thresholds, and interpretation notes are all in one place.

1. Click ⟳ Analyse Now to compute pairwise correlations across the z-score series of all analytes (last 30 runs each).
2. Review the Pearson Correlation Heatmap — strong correlations (≥0.7) between unrelated analytes are the signature of a systemic event rather than a single bad analyte.
3. Check the Z-Score Drift Overlay chart for analytes moving in the same direction at the same time.
4. Read the Auto-Generated Interpretation banner — it's a plain-language summary, written from the correlation numbers above it, of which analyte pairs are most strongly linked and what that pattern typically indicates (e.g. reagent lot, calibrator, environmental).
5. Open the Analyte Pair Correlation Detail table and use the Strong Only filter to focus on clinically actionable pairs.

1. Select the Analyte you want a frequency recommendation for.
2. Set its Clinical Risk Category: Critical (e.g. K+, troponin, glucose, Na+), High (e.g. ALT, creatinine, bilirubin), Moderate, or Low.
3. Set your Daily Patient Volume and Analyser Stability History (how often it has failed in the last month).
4. Enter your current QC runs per day for comparison.
5. Review the Frequency Recommendation panel and the Detection Delay — Real-Time Conversion table, which translates each method's run-based detection delay into hours/days at your actual testing pace.

1. Choose the Report Period (last 30/90 days, or all available data).
2. Fill in your Laboratory Name, Department, and Authorising BMS — these appear on the cover page.
3. Select the Accreditation Standard (ISO 15189:2022 or UKAS Medical).
4. Tick which sections to include: Executive Summary, Per-Analyte Performance, Alert Log, Sigma Metric Summary, Audit Trail.
5. Click Preview to check the report, then Generate PDF to download the final document.