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Enregistrement W4409219634 · doi:10.32920/28745411

Development of a Unique Indicator Label

2025· preprint· en· W4409219634 sur OpenAlexaboutno aff
Martin Habekost, Jason Lisi, Krishan Rampersad

Notice bibliographique

Revuenon disponible
Typepreprint
Langueen
DomaineMedicine
ThématiqueInfection Control in Healthcare
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBusinessComputer science

Résumé

récupéré en direct d'OpenAlex

<p>Ryerson University has teamed up with Lunanos Inc., a Toronto-based company, to develop a method for the production of its IndiClean label on a flexographic label press. This involved research into the right combination of anilox rollers to be used, to the correct screen tint for applying the proprietary indicator ink and also finding the best way to apply a protective layer over the label, so that the indicator functions to specifications. The application of the protective layer involved some innovative thinking in regards to the application and die-cutting process. </p> <p>Many surfaces in medical facilities are consider high traffic touch points and need to be disinfected on a regular basis to avoid the spreading of germs and infections. Environmental surfaces provide an excellent environment for pathogenic microbes to live and reproduce. Many microbes are able to survive for extended periods of time on everyday surfaces such as bed rails and ultrasound machines. These potentially multi-drug resistant bacteria are then able to spread by contact with patients, staff, and visitors, resulting in healthcare-associated infections (HAIs). </p> <p>Improper cleaning can lead to increased HAIs. HAIs are the fourth largest cause of death in developed countries, resulting in more deaths than breast cancer, AIDS, and traffic accidents combined. Even though 30-50% of these cases are preventable, they affect 1 in 10 Canadian hospital admissions, leading to 8000 deaths every year, and it is estimated that an HAI can increase individual treatment costs by $6,000 to $45,000 as well as lengthen in-patient treatment time by 4 to 14 days.</p> <p>Infection prevention and control professionals have said that tracking the cleaning of the over 10,000 pieces of equipment in a hospital is very difficult, pointing particularly to mobile equipment—like IV poles, carts, and wheelchairs. Although hospital cleaning personnel know that they play a key part in patients’ care, they are often under tremendous time pressure to complete their tasks, and they are looking for an automatic method to note whether a particular surface needs to be re-cleaned. Currently, there is no product on the market that can address that issue. Traditional methods, like log sheets or writing time of cleaning on pieces of tape, require hospital staff to remember to pause cleaning in order to make notes; while advanced methods, like using proximity sensors, are very costly and require extensive training.</p> <p>Lunanos Inc. has developed a proprietary indicator coating technology, which they have incorporated into a prototype label, that will help healthcare facilities improve disinfection procedures of environmental surfaces by clearly identifying surfaces and equipment that require cleaning. The label (IndiClean) can be placed upon numerous surfaces, including pieces of mobile equipment that travel from room to room in hospitals. When a staff member uses a liquid disinfectant to wipe down a surface, the proprietary polymer technology that coats IndiClean will cause a visible color change. The company is currently developing a method to control the time it takes for the color to return to the initial state, allowing for differences in each facility’s protocols regarding when cleaning is required. Cleaning staff will be trained to identify the initial color (i.e. before cleaning), and to proceed with cleaning after observation of the “unclean” color. The </p> <p>labels automatically activate, preventing the need for staff members to remember what they need to clean and what they have cleaned already. Training will be provided to staff to strategically place labels in a strategic location on each high traffic touch point surface in order for staff to easily see the indicator during their normal routine. IndiClean has been designed clearly such that there will be minimal difficulties with interpreting its message. Currently, there are no such cleaning indicator products on the market, making IndClean a whole new product class.</p> <p>Lunanos Inc. was successful in creating handmade prototypes of their label; however, these prototypes varied considerably in consistency due to the uncontrollable variability associated with the hand craftingprocess. In addition, the current method of assembly does not allow for mass production of the labels, nor is it economically viable. Ryerson’s role in this project was to develop a process that would allow consistent and repeatable results for generating good labels at a mass scale, at a reasonable cost-per-unit.</p> <p>This research paper details the research, testing, and progress to date associated with developing a successful, reliable, and reproduceable IndiClean label.</p>

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,632
Score d'incertitude au seuil0,726

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,054
Tête enseignante GPT0,379
Écart entre enseignants0,325 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeAutre devis
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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