Assessment of a new point-of-care system for detection of prostate specific antigen
Bibliographic record
Abstract
BACKGROUND: Measurement of the prostate specific antigen (PSA) remains an important tool in prostate cancer (PC) diagnosis. Due to limited availability of laboratory devices in an outpatient setting, compact and easy-to-handle point-of-care (POC) systems are desirable. Recently, a chip for PSA measurement on the concile® Ω100 POC reader platform was introduced. To investigate the clinical applicability, we evaluated the system in a consecutive cohort of patients undergoing PSA measurement in our outpatient clinic. METHODS: Between 07/2014 and 01/2015, PSA was analyzed in a total of 198 patients by the POC reader system and in parallel by an Immulite 2000® and Centaur® standard laboratory system, respectively. By standard (Immulite®) measurement, 67 (34,2 %) had PSA > 4 ng/ml and 131 (65,8 %) had PSA ≤ 4 ng/ml. Results were correlated by linear regression analyses for all patients and within PSA subgroups. For patients with available prostate histology after PSA measurement (n = 68), receiver-operating characteristic curves were created and area under the curve (AUC), sensitivity and specificity for the prediction of PC at best cut-off value were calculated. RESULTS: The coefficients of determination (r(2)) for the POC device compared to laboratory testing were 0.72 (Immulite®) and 0.63 (Centaur®), respectively (both p < 0.0001). In the PSA range of ≤4 ng/ml, the observed correlations were 0.75 and 0.70, respectively. For the POC test system, AUC for detection of PC was calculated with 0.745 while the standard laboratory tests showed 0.778 (Immulite®) and 0.771 (Centaur®). At best cut-off of 3.64 ng/ml, PSA analysis by the POC system showed a sensitivity of 85.7 % and a specificity of 66.7 %. CONCLUSIONS: The POC system obtained good concordance to elaborate laboratory measurement. In a screening scenario, the system provides quick and reliable PSA measurement, especially in the PSA range up to 4 ng/ml.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".