Psychosocial and quality of life impact of scars in the surgical, traumatic and burn populations: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Despite the fact that millions of scars affect individuals annually, little is known about their psychosocial impact and overall quality of life (QOL) on individuals. Scars from multiple aetiologies may cause psychiatric and emotional disturbances, can limit physical functioning and increase costs to the healthcare system. The purpose of this protocol is to describe the methodological considerations that will guide the completion of a scoping review that will summarise the extent, range and nature of psychosocial health outcomes and QOL of scars of all aetiologies. METHODS AND ANALYSIS: A modified Arksey and O'Malley (2005) framework will be completed, namely having ongoing consultation between experts from the beginning of the process, then (1) identifying the research question/s, (2) identifying the relevant studies from electronic databases and grey literature, with (3) study selection and (4) charting of data by two independent coders, and (5) collating, summarising and reporting data. Experts will include a health information specialist (TAW), scar expert (JSF), scoping review consultant (SCK), as well as at least two independent coders (NZ, AM). ETHICS AND DISSEMINATION: Ethics approval will not be sought for this scoping review. We plan to disseminate this research through publications, presentations and meetings with relevant stakeholders.
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 | 0.116 | 0.080 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.046 | 0.010 |
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".