Standardization of clinical protocols in oral malodor research
Bibliographic record
Abstract
The objective of this study is to standardize protocols for clinical research into oral malodor caused by volatile sulfur compounds (VSCs). To detect VSCs, a gas chromatograph (GC) using a flame photometric detector equipped with a bandpass filter (at 393 nm) is the gold standard (sensitivity: 5 × 10(-11) gS s(-1)). The baselines of VSC concentrations in mouth air varied considerably over a week. When the subjects refrained from eating, drinking and oral hygiene including mouth rinsing, the VSC concentrations remained constant until eating. Over a 6 h period after a meal, VSC concentrations decreased dramatically (p < 0.01). These results point to optimal times and conditions for sampling subjects. Several portable devices were compared with the measurements by the GCs. Portable GCs demonstrated capabilities similar to those of the GCs. We also applied the recommended protocols described below to clinical research testing the efficacy of ZnCl(2) products, and confirmed that using the recommended protocols in a randomized crossover design would provide very clear results. Proposed protocols include: (a) a short-term study rather than a long-term study is strongly recommended, since the VSC concentrations are constant in the short term; (b) a crossover study would be the best design to avoid the effects of individual specificities on each clinical intervention; (c) measurements of VSCs should preferably be carried out using either a GC or portable GCs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.204 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".