Secondary Autologous Fat Grafting for the Treatment of Chin Necrosis as a Consequence of Prone Position in COVID-19 Patients
Bibliographic record
Abstract
Due to the spread of the coronavirus disease 2019 pandemic, an increasing number of ill patients have been admitted to intensive care unit requiring mechanical ventilation. Although prone positioning is considered beneficial, long periods in this position may induce important complications, including pressure ulcers in high-risk and uncommon body areas. We report five cases of pressure ulcer necrosis of the chin in coronavirus disease 2019 patients as a consequence of mechanical ventilation in prone positioning using autologous fat grafting (AFG) as a secondary technique. A series of five patients with secondarily-healed chin necrosis treated by AFG between February and June 2020 were reviewed. All patients had been treated initially with surgical debridement followed by conservative treatment. Secondary AFG was performed to reduce patient's pain, improve chin contour-projection, and minimize cosmetic sequelae and scarring. Patient satisfaction was assessed using a five-point Likert scale (0-4). Vancouver scale was used to evaluate the chin scars clinically. The average amount of fat injected into the chin area was 8.1 ± 2.0 ml. At 6-month follow-up, all patients were mostly satisfied (average Likert-scale 3.2 ± 0.4). Based on the Vancouver scale, improvement of the chin scar from 9.5 ± 0.8 to 4.7 ± 0.8 was found. We report a positive experience with secondary AFG for correction of painful and unaesthetic scarring and contour abnormality following surgical debridement and secondary-intention healing of chin pressure ulcers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".