Evaluation of the Tempo® System: Improving the Microbiological Quality Monitoring of Human Milk
Bibliographic record
Abstract
Background: Bacteriological testing of donor human milk is mostly done both before and after pasteurization to control contamination in the end-product and meet the microbiological standards. Although the plate count method represents a reliable and sensitive technique and is considered the gold standard for bacteriological testing, it is recognized for being time-consuming and requiring qualified personnel. Recently, faster testing technologies, mostly geared towards the food industry, have been developed. Among these, the bioMérieux TEMPO® system uses the most probable number method to assess microbiological content in a semi-automated fashion. Objective: The performances of the TEMPO® system in enumerating bacterial quality indicators in human milk were assessed and compared to the reference plate count method. Methods: Naturally and artificially contaminated human milk samples were used to compare the analytical performances of the TEMPO® system to the plate count technique. More specifically, bacteria belonging to the genera Bacillus, Enterobacteriaceae, Staphylococcus aureus and total aerobic flora were screened using both methods. Bacteria isolated on agar plates containing selective media were identified by supplemental testing. Bacterial testing results and method parameters were compared using linear regression analyses and Bland-Altman approaches. Results: Naturally contaminated milk samples (n = 55) tested for total aerobic flora showed 0.9). At lower levels of bacterial contamination, the TEMPO® method precision (C.V. 83%) were comparable to plate counts. Conclusions: The analytical performances of the TEMPO® system for human milk bacteriological testing are equivalent to the reference plate count method. Results from the TEMPO® system are available within a 24-hour turnaround time from sample inoculation without the need for further supplemental testing, suggesting that this semi-automated method could be implemented within milk bank operations as an in-process monitoring technology to optimize end-product quality and safety.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".