An evaluation of sampling and laboratory procedures for determination of heterotrophic plate counts in dental unit waterlines.
Bibliographic record
Abstract
BACKGROUND: The high numbers of heterotrophic microorganisms that have been cultured from dental unit waterlines (DUWs) have raised concern that this water may exceed suggested limits for heterotrophic plate counts (HPCs). The main purpose of this investigation was to examine HPC variability in DUWs and to examine in detail the effect of laboratory processing of water samples on HPC values. METHODS: Water samples were collected from dental offices either at the beginning of or during the clinic day and were transported to the laboratory, where they were analyzed. RESULTS: Measuring HPC levels within an office would involve testing all units, because significant differences were found between units connected to the same municipal water supply. Within a unit, the average microbial count from high-speed lines was approximately twice the average count from air/water lines. The laboratory processing of water samples significantly affected the numbers of heterotrophic microorganisms that were recovered. Incubation temperature, time and media, as well as neutralization of residual chlorine, all had significant effects on the HPC values. However, no significant differences in microbial counts were found between samples plated with the spread plate method on R2A agar and those plated with the pour plate method with Plate Count Agar. CONCLUSIONS: Dental organizations have suggested target limits in terms of numbers of heterotrophic microorganisms recovered in water from dental units, but standards for laboratory handling must be established as well. A protocol for sample collection and laboratory handling is proposed.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".