Workflow of Healthcare Professionals Utilizing Fluorescence Imaging for Detecting Wound Bacterial Infections
Bibliographic record
Abstract
Background Many wound care specialists use fluorescence imaging (FLI) to complement the assessment of clinical signs and symptoms in the detection of wound infections, providing clinical value to patient management and healthcare economic benefits. Objectives To assist other wound care practices in adopting FLI technology, we addressed four key questions related to the activities essential for successful implementation: (1) Who is responsible for each activity? (2) How long does the overall imaging procedure take? (3) How much time does each activity require? (4) What patient and wound variables might influence the time needed for FLI? Methodology To answer these questions, surveys were collected via Qualtrics from eight physicians representing unique practices who had been trained on MolecuLight FLI (Toronto, ON, Canada) and had between 0.5 and 5 years of real-world patient experience with the technology. This study employed a cross-sectional, electronic survey design to evaluate physicians' experiences with FLI in wound care. Results The FLI workflow can be divided into 10 activities. In all practices, physicians are responsible for educating patients and obtaining informed consent. In most practices, physicians are responsible for all other activities related to FLI. The overall procedure requires an average of 28.8 minutes of physician time for a single wound. Patient variables affect procedure time. Conclusions Clinical and economic benefits, including the ability to modify wound treatment plans and reduce costs associated with managing infection-related complications, can be achieved with fluorescence imaging (FLI) to detect wound infections. This requires a relatively small time investment from physicians and is supported in some practices by nurse practitioners (NPs) and physician assistants (PAs).
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 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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".