A-223 Laboratory Process Tracker (LPT): a tool for real-time tracking of samples, instruments, and workflow steps used in clinical mass spectrometry testing
Bibliographic record
Abstract
Abstract Background Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has seen ever increasing adoption by clinical laboratories. Most MS-based tests are laboratory-developed tests (LDTs). Common pre-analytical workflows include multiple method-specific steps such as sample aliquoting, extraction, dry-down, reconstitution and data acquisition on LC-MS/MS. Even when individual steps are automated, most laboratories find it challenging to track them to detect and correct errors in real-time. This remains a largely manual process. Here we describe features and benefits of the in-house developed software, Laboratory Process Tracker (LPT), which uses a system of barcodes to enable real-time tracking of LC-MS/MS batches throughout the sample preparation and data acquisition steps. Methods LPT is software developed using .NET 6 and Visual Studio Code. The LPT software settings were customized to reflect method-specific workflow and step-specific acceptance criteria. The following describes how it works for each method. 1. LPT generates 2D barcode labels that are assigned to each instrument and each trained user. 2. A new batch is created in LPT by scanning the user barcode, selecting the pre-programmed method name, uploading the batch specific sample list, and entering the batch number. The batch-specific barcodes are printed to label primary sample racks and 96-well plates for secondary samples. 3. The batch processing is then tracked step-by-step by scanning the barcodes of instruments, users, and rack/plate(s)/sample at the beginning of each step. LPT flags a step if the value entered fails to meet the passing criteria. The user can determine how to correct the error; it may require restarting the step, the whole batch, or even aborting the batch. 4. Finally, batches with addressed error flags are made available for manual review and sign-off after the batch is completed. Batches without errors are auto signed off by LPT. Results LPT was extensively validated and has been used in our laboratory for six LC-MS/MS methods since 2022. During this time, LPT has been used to successfully track more than 200 batches of samples per month. Less than 10% of all batches were flagged since they failed one or more acceptance criteria built into the software. The most common errors are due to batch mix-up, one step skipped or repeated, wrong instrument used, and processing time not matching the time allowance. Since its implementation, LPT has helped the lab achieve time and cost savings in error detection and mitigation. The additional benefits of LPT include a daily dashboard for tracking the status of all batches, summary of common operation errors, help with investigation and troubleshooting. Conclusion LPT software is an end-to-end pre-analytical workflow tracking tool. It is intuitive and user-friendly. In our clinical MS laboratory, it contributes to quality improvement, risk management and cost reduction. Collaboration among operations, clinical/scientific, and IT teams is essential in development, validation and ongoing improvement of the tool. We propose that software like LPT could be applied to any clinical laboratory workflow that includes a linear sequence of steps if gaps exist in process tracking.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.027 |
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".