487 The Matching Assessment using Photographs with Scars (MAPS) App: Reliability Testing
Bibliographic record
Abstract
The Matching Assessment using Photographs with Scars (MAPS) is a scar assessment tool that was developed in 2005. The MAPS has been recommended as one of the preferred scar assessment tools in a recent systematic review as it enables accurate relocation and reassessment of the scar. It is used in clinical practice and research across Australia, however, its distribution has been hampered by the need to utilise an A4 paper manual. With the use of electronic records and increasing uptake of the use of tablets and applications (App’s) in health the MAPS manual has been translated into an App format. In addition to the MAPS, the Modified Vancouver Scar Scale (mVSS) and a Patient Reported Outcomes questionnaire was incorporated into the App to produce a comprehensive scar assessment package: ClinMAPS™Pro. At the time of development no other scar assessment Apps were available, making this the first of its kind. To ensure the digitised version of the MAPS is a reliable scar assessment, a reliability study for intra- and inter-reliability was required. The digital version of the MAPS within ClinMAPS™Pro was used for intra- and inter-rater reliability testing. Convenience sampling was utilised to recruit burns patients representing 42 scars, based on pre-determined power calculations. Three therapists, one experienced and two novice, acted as the assessors. The primary investigator marked out 3-6 scars on each patient and recorded the location via photo to ensure accurate relocation at re-test. Each therapist then assessed the pre-selected scars with the digitised MAPS. Re-assessment of the same scar sites occurred 3-7 days later. Assessment scores for each parameter were recorded by the primary investigator. Reliability testing results for the new electronic MAPS within the ClinMAPS™Pro App will be presented. The digitisation of a previously paper based, costly scar assessment manual ensures that MAPS is now readily available for clinicians and researchers internationally. With the addition of the mVSS and the Patient Reported Outcomes questionnaire, ClinMAPS™Pro is a comprehensive scar assessment package. Results of the reliability testing of the MAPS component confirm its intra- and inter-rater reliability The ClinMAPS™Pro contains scar assessment tools that can be applied to clinical practice and research. It can be easily integrated into electronic records or printed for paper records due to its functionality. As it can be completed on a mobile device it is easily accessible for clinicians and researchers.
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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 | 0.043 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".