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Record W4409219634 · doi:10.32920/28745411

Development of a Unique Indicator Label

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

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessComputer science

Abstract

fetched live from 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>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.054
GPT teacher head0.379
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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