Current News Recommendations for fish spa operators developed by multi-agency working group Infection Reports
Bibliographic record
Abstract
Recommendations for fish spa operators developed by multi-agency working group The extent to which the use of live fish in “fish pedicure ” procedures may be associated with a risk of transmission of a range of infections has been considered by a multi-agency working group whose conclusions have been published on the HPA website [1,2]. Fish pedicures – involving one or more individuals immersing their feet in a tank containing live Garra rufa fish which can feed on, and thus remove, dead and hardened skin – have been banned in parts of the USA and Canada. However, use of such procedures as a treatment for certain skin conditions (eg psoriasis) is longestablished in other parts of the world (Turkey, India and the Far East). In the UK, fish pedicures are being offered by an increasing number of establishments, including dedicated salons, beauty therapists and hairdressers. The Health Protection Agency-led working group was convened following enquiries from environmental health practitioners concerned about the potential hazard to public health. The potential for transmission of a range of infections either from fish-to-person (during the nibbling process), water-to-person (from the bacteria that can multiply in water), or person-to-person (via water, surrounding surfaces and fish) in fish spa settings was considered.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.105 | 0.081 |
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".