Genuine halitosis, pseudo-halitosis, and halitophobia: classification, diagnosis, and treatment.
Bibliographic record
Abstract
Although tongue brushing and appropriate mouthrinses are both important and basic treatment measures for halitosis, other dental treatments are sometimes required. The treatment of genuine halitosis caused by oral conditions is not complex. In addition to genuine halitosis patients, psychosomatic halitosis patients also visit dental practitioners. Although psychosomatic halitosis is out of the treatment realm of dental practitioners, patients with this condition will still seek help from a dental practitioner. They often only receive treatment for genuine halitosis without referral to a psychological specialist. If these psychosomatic halitosis patients are incorrectly managed, the psychological condition might become worse than before the visit. To avoid the mismanagement of halitosis patients, classifications of halitosis patients have been established. Genuine halitosis was subclassified as physiologic halitosis and pathologic halitosis. Pathologic halitosis was further categorized to oral pathologic halitosis and extraoral pathologic halitosis. Both pseudo-halitosis and halitophobia patients complain of the existence of halitosis, which is not offensive. Pseudo-halitosis cannot be treated by dental practitioners, and halitophobia patients must be referred to psychological specialists. Clinicians need to examine the psychological condition of halitosis patients at the initial patient visit. A questionnaire prepared for the clinic at the University of British Columbia was found to be advantageous for this purpose.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".