Performance Data

Agilent performance claims need methods, conditions, and acceptance rules

This review framework separates a catalog value from evidence that can be reproduced in a specific laboratory workflow.

Structured evidence

Record the condition beside every number

Values are example protocol targets from project requirements, not declared specifications for every Agilent model.

Metric	Example requirement	Test condition	Evidence to retain	Decision limitation
Analyzer throughput	600–2,000 tests/hour	Defined assay mix, STAT rate, reruns, calibration, and staffing	Timed run log and exception count	Headline maximum may omit interruptions
Precision	Site-defined CV threshold	At least two concentration levels across repeated runs	Raw results, mean, SD, CV, operator, lot, and date	One matrix or level does not establish full-range precision
Detection capability	Method-specific LOD/LOQ	Blank and low-level replicates with declared calculation	Protocol, raw signals, exclusions, model, and calculation	LOD does not equal clinical decision performance
Method comparison	Predefined bias limits	Samples spanning the reportable range	Deming regression, Bland–Altman plot, residual review	Correlation alone can hide systematic bias
LIS interface	100% pass for approved cases	Normal, amended, rerun, rejected, and outage scenarios	Message capture, expected result, actual result, approval	Lab test success does not cover every production exception

Reproducibility checklist

A review another team can repeat

Environment: record temperature, humidity, power, network, software version, instrument configuration, and maintenance state.
Materials: record sample matrix, concentration, preparation, calibrator and reagent lot, storage, and handling time.
Method: predefine replicate count, run order, exclusions, calculation, bias or CV threshold, and exception handling.
Data: retain raw results, audit trail, failed runs, repeats, calculations, approvals, and deviations—not only the summary.
Boundaries: disclose matrix effects, carryover, throughput interruptions, interference, lot variation, operator effects, and integration dependencies.
Governance: assign protocol approval, execution, deviation assessment, release decision, and post-change revalidation.

Selection trade-offs

A higher-throughput platform can improve batching efficiency but may increase dependency on automation, reagent logistics, and centralized downtime planning. A smaller distributed platform may improve local turnaround yet require more instruments, competency records, QC events, and interface endpoints. Compare both paths against the same volume and failure scenarios.

Agilent performance claims need methods, conditions, and acceptance rules

Record the condition beside every number

A review another team can repeat

Selection trade-offs

Request model-specific Agilent evidence for your protocol