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Record W4387692965 · doi:10.1111/iwj.14446

Point‐of‐care fluorescence imaging to optimise wound bed preparation prior to cellular and/or tissue‐based product (CTP) application

2023· letter· en· W4387692965 on OpenAlexaff
Thomas E Serena, Keith G Harding, Douglas Queen

Bibliographic record

VenueInternational Wound Journal · 2023
Typeletter
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsMaple Leaf Medical Clinic
Fundersnot available
KeywordsMedicineWound careDebridement (dental)Diabetic footWound healingHealth careIntensive care medicineSurgeryDiabetes mellitus

Abstract

fetched live from OpenAlex

My son's interpretation of ‘MAC Attack’ differs drastically from mine. His comes from a ravenous urge for two all-beef patties on a sesame seed bun, whereas I am referring to one of the endless limitations placed on my practice by Medicare administrative contractors (MACs). The most recent ‘MAC Attack’ by Novitas, First Coast Service Options and CGS, representing 14 US states, restricted the use of Cellular and/or Tissue-Based Products (CTPs), also known as ‘skin substitutes’, to four applications per diabetic foot or venous leg ulcer.1 These new local coverage determinations (LCDs) are effective as of 17th September 2023. Failure to achieve success after four CTP applications will result in the loss of patient access to this therapy—both ‘MAC Attacks’ have potential adverse health effects. But before villainizing the MAC medical directors for their decision, a review of the literature is warranted. In truth, CTPs used without proper wound bed preparation often fail. In fact, the results are worse than if a wound was never treated with a CTP.2 The key to success in healing wounds with CTPs is wound bed preparation: debridement, control of bacterial burden, maintaining an appropriate moisture balance, off-loading for diabetic foot ulcers and compression for venous leg ulcers. Reduction of bacterial load is crucial prior to the application of CTPs; however, wound care specialists often rely solely on clinical signs and symptoms to detect bacterial load. The ability to detect clinically significant levels of bacteria using examination is poor (sensitivity less than 15%).3 Wound cultures are equally inaccurate,4 and it takes days for the results to return. The national and local coverage determinations for CTP use have required control of bacterial burden as a condition for reimbursement for years; however, clinicians have used and continue to use inaccurate and unreliable methods for determining bacterial load. The decision on when to apply a CTP is at best haphazard. The fault of this ‘MAC Attack’ lies with the wound care community and the slow adoption of diagnostics. The most studied diagnostic in the detection of bacteria in acute and chronic wounds is fluorescence imaging—a point-of-care, non-invasive, modality that safe uses violet light to detect bacterial loads greater than 104 CFU/g.3 At this level, bacteria elicit changes at the cellular level that result in tissue damage and healing arrest, often without exhibiting signs of overt infection. This number of bacteria was recently termed chronic inhibitory bacterial load (CIBL).5 Clinical signs and symptoms of bacterial load are inaccurate in non-healing wounds and are often completely absent in immunocompromised patients.3 The ability to detect a clinically significant level of bacteria, CIBL, prior to the application of a CTP using only signs and symptoms of infection is poor; however, fluorescence imaging can improve the detection of bacteria by fourfold to sevenfold3 irrespective of clinical expression and throughout all skin tones. Despite being an accurate, bedside method of bacterial detection, the adoption of fluorescence imaging has been slow. Real-time fluorescence imaging has been reported to improve both CTP and autologous skin grafting outcomes based on the presence or absence of bacterial fluorescence prior to application.6, 7 Further, fluorescence imaging improves reduction of bacterial burden, as demonstrated in numerous publications, by accurately identifying and localising high bacterial loads and guiding clinicians in the process of removing bacteria in real time.8, 9 A randomised control trial found that the most common intervention prompted by fluorescence imaging was debridement, and patients who received the imaging intervention showed improved healing (twice as many DFUs healed at 12 weeks than the standard of care).10 Wound specialists must expand their toolkit to optimise wound bed preparation prior to the application of CTPs or face stricter and broader restrictions. Fluorescence imaging in combination with physical examination enhances the identification of bacteria, which in turn improves wound healing outcomes with CTPs. Finally, as the wound care community builds a robust body of evidence on the optimisation of CTPs, the evidence garnered can be used to assuage the next ‘MAC attack.’

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.735
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.023
GPT teacher head0.344
Teacher spread0.322 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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