12 Years of ablative fractional CO2 laser Practice: Logistics, lessons and evolving model of care
Bibliographic record
Abstract
• Ablative fractional CO 2 laser (AFCO 2 L) services improve equity of access to scar treatment. • AFCO 2 L service resulted in reduced burn scar operative reconstructive cases. • Younger, female patients more likely to receive AFCO 2 L. • Patients with slower healing burns more likely to receive surgery and AFCO 2 L. Ablative fractional CO 2 lasers (AFCO 2 L) have been shown to improve burn hypertrophic scars significantly. In this paper we describe the journey of setting up the laser service for burns patients, considerations in patient selection, treatment algorithms, and lessons learned. This study is a retrospective cohort study including all patients who received AFCO 2 L at the Western Australian (WA) Statewide Adult Burn Unit since the start of the program in 2013–2024. Descriptive statistics present the number, timing and settings of AFCO 2 L events, as well as patient, injury, and treatment characteristics. Further, the profile of patients who underwent laser treatment was compared to those who did not, during the study period. Since the introduction of the AFCO2L, a total of 4005 laser sessions involving 837 burns patients has been completed in WA. The majority were performed as an outpatient (66 %), with the proportion and total numbers increasing with time to 2021. Compared to those not receiving laser for their scars, AFCO2L was more likely applied to younger (p < 0.0001), female (p < 0.0001) patients with higher %TBSA burns (p < 0.0001) involving multiple anatomic areas (p = 0.001), more often requiring surgery (p < 0.0001) and longer times to heal (p < 0.0001). In 2013, 100 % of all lasers were provided as an inpatient, under general anesthetic with an average age of scar > 5000 days. By 2023/4, only 18 % required an inpatient stay and the average age of scar was 111 days. The SABU team evolved AFCO 2 L therapy into the model of care over time to achieve earlier, more equitable delivery of laser treatments to 80 % of patients as outpatients, supported by extensive multidisciplinary team involvement.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".