Commentary on: The Risk of Skin Necrosis Following Hyaluronic Acid Filler Injection in Patients With a History of Cosmetic Rhinoplasty
Bibliographic record
Abstract
The present article consists of a two-year retrospective chart review of a single center’s vascular adverse events (VAE) associated with facial hyaluronic acid (HA) filler injections.1 The authors identified 7 patients (and only 7 patients in all) who fit their definition of a VAE. Upon review of the clinical surgical history of each of these 7 patients, they found that all 7 were former rhinoplasty patients. The authors do not report the total number of patients who were injected during this two-year period, nor do they report how many of their HA filler patients had an uneventful treatment despite their history of rhinoplasty. Unfortunately, this information does not give us the risk associated with fillers in former rhinoplasty patients. There are few estimates of the rate of VAE in filler patients in the literature, estimated as 3 out of 1000 injections.2 The values of the missing numbers in Table 1 make a huge difference to the interpretation of the results. As a valuable heuristic (ie, “rule of thumb”), it is useful to consider correlative data in the form of a fourfold (contingency) table, with the rows and columns divided as shown in Table 1.
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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 | 0.001 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.042 | 0.023 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".