Quality Assurance System Using Statistical Process Control: An Implementation for Image Cytometry
Bibliographic record
Abstract
AIMS: Optical technologies have shown some promise for improving the care of cervical neoplasia. We are currently evaluating fluorescence and reflectance spectroscopy and quantitative cyto-histopathology for cervical neoplasia screening and diagnosis. Here we describe the establishment and application of a quality assurance (QA) system for detecting system malfunctions and assessing the comparability of four image cytometers used in a multicenter clinical trial. METHODS: Our QA system involves three levels of evaluation based on the periodicity and complexity of the measurements. We implemented our QA system at three image cytometers at the British Columbia Cancer Agency and one at M.D. Anderson Cancer Center. The measurements or tasks were performed daily, monthly, and semi-annually. The current and voltage of the lamp, the calibration image characteristics, and the room temperature were checked daily. Long-term stability over time, short-term variability over time, and spatial response field uniformity were evaluated monthly. Camera linearity was measured semi-annually. Control charts based on statistical process control techniques were used to detect when the system did not perform optimally. RESULTS: Daily measurements have shown good consistency in room temperature, lamp and calibration behaviour. Monthly measurements have shown small coefficients of variation between and within the four devices. There have been greater differences between sessions than within sessions. Comparability among the four systems is reasonably good. Semi-annual measurements have shown stable camera linearity. QA events were detected using the QA system. Multiple examples of event detection leading to correction of system malfunction are described in this report. CONCLUSIONS: QA programs are critical for ensuring data integrity and therefore for the conduct of multicenter clinical trials.
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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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".