Response by Hadid et al to “Hemostatic Nets in Facelifts: A Systematic Review and Meta-Analysis of Postoperative Complications and Patient Outcomes” by Caimi et al
Bibliographic record
Abstract
We read with interest the recent article by Caimi et al, “Hemostatic Nets in Facelifts: A Systematic Review and Meta-Analysis of Postoperative Complications and Patient Outcomes.”1 While the topic is of clinical relevance, we identified several methodological concerns that we believe undermine the reliability of the authors’ conclusions. Most notably, 2 of the included studies by Auersvald et al (2012 and 2014) appear to draw from overlapping patient populations.2,3 Both originate from the same surgeon and institution, cover overlapping study periods, and, most strikingly, report control groups of identical size (n = 120), identical mean age to 2 decimal places (55.40 years), and identical age range (33-77 years) as seen in the paper's Table 1.1 The probability of these parameters being identical in 2 independent control cohorts is extremely low, strongly suggesting that the same patients were included in both publications. Inclusion of these likely duplicate patients appears to have had a significant influence on the reported meta-analytic results. For example, 57.4% (651/1135) of patients contributing to the pooled hematoma rate in Figure 2, and as many as 89.1% (651/731) of those in the transient postoperative changes analysis in Figure 4, are likely duplicates from overlapping patient cohorts. If confirmed that these cohorts are overlapping, we respectfully suggest that the authors reanalyze their data in a response letter with one of the studies removed to provide an accurate and unbiased synthesis. Further, the risk-of-bias assessment raises concerns. The authors applied the Newcastle-Ottawa Scale uniformly to all included studies, despite substantial variation in study design, including a randomized controlled trial, prospective-retrospective cohort studies, and an uncontrolled case series. The Newcastle-Ottawa Scale is not validated for randomized trials or case series, and its use in these contexts produces misleading quality scores and creates false equivalence between fundamentally different levels of evidence.4 Established, design-specific tools such as the Cochrane RoB 2 for randomized trials and JBI checklists for case series would have provided a more accurate and defensible evaluation of bias.5 Given these issues, particularly the apparent duplication of patient data, we urge caution in interpreting the reported findings. Addressing these concerns in future meta-analyses will be critical to ensuring that conclusions about the efficacy and safety of hemostatic nets are both robust and reproducible. The authors declared no potential conflicts of interest with respect to the research, authorship, and publication of this article. The authors received no financial support for the research, authorship, and publication of this article.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | medium |
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 | 0.018 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.020 | 0.023 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".