544 Improvement of Skin Quality in Patients Undergoing Nasal Fat Grafting: A Systematic Review Protocol
Bibliographic record
Abstract
Abstract Aim Primary aims: to understand the effects of adipose tissue or its derivatives on nasal skin quality, and whether lipofilling may improve nasal skin quality. Secondary aims: to understand whether post-lipofilling nasal skin improvement can be observed and quantified in both good quality and scarred nasal skin, to investigate if changes are related to the differing levels of preoperative skin quality (unscarred vs scarred), to review outcomes for different types of fat harvesting and processing techniques prior to grafting and identify complications following nasal lipofilling. Method A search strategy will be developed to search MEDLINE, Embase, Web of Science, and the Cochrane Central Register of Controlled Trials, followed by a grey literature search, to identify relevant publications. Two researchers will review titles, abstracts, and full-texts of the resulting publications, against the inclusion and exclusion criteria (a third researcher will decide where there are disagreements), as part of an independent two-person three-stage assessment strategy. Non-English/ Portuguese/ French articles, abstracts, letters, editorials, and expert opinions will be excluded. Results This systematic review has been registered on the PROSPERO International Prospective Register of Systematic Reviews (CRD 42021290198). Conclusion Adipose tissue has regenerative potential; it has been suggested there may be improvement in nasal skin quality following fat tissue grafting into the nasal region. Primary and secondary rhinoplasty procedures are being increasingly performed and multiple procedures may result in nasal skin damage, which should be addressed during secondary reconstruction. Understanding whether lipofilling improves nasal skin quality in nasal reconstruction could support its use in appropriate patients undergoing rhinoplasty.
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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 | 0.048 | 0.056 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.015 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.055 | 0.006 |
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".