Forehead Reduction: A Systematic Review and Meta-Analysis of Outcomes
Bibliographic record
Abstract
Importance: Forehead reduction, or hairline lowering surgery, is becoming more popular as a cosmetic procedure for patients with disproportionately large foreheads. A large forehead can make a patient appear older, be masculinizing, and less attractive. Objective: To quantify reported outcomes in patients undergoing forehead reduction. Methods: We performed a systematic review and meta-analysis of adults undergoing forehead reduction. A review protocol was published in PROSPERO (CRD42020183366). A research librarian created search strategies in multiple databases. Abstracts and full texts were reviewed in duplicate. The Newcastle-Ottawa scale and Cochrane Collaboration Risk of Bias tool were used. Random effects meta-analyses were performed. The primary outcome was amount of reduction. Other extracted data included study type, location, sample size, scalp fixation method, incision, complications, follow-up time, percentage female, and age. Results: Our search strategy found 376 unique citations, and 8 studies were included. All eight were retrospective cohort studies, comprising 882 patients (range 5–525). Study quality was high, and risk of bias ranged from unclear to high. Four studies were included for meta-analysis, totaling 801 patients. Mean amount of reduction was 1.6 cm (95% confidence interval: 1.4–1.8). Complications included temporary and permanent alopecia, unacceptable scarring, persistent paresthesia, and hematoma. The pooled complication rate was 1% or less. Conclusion: Forehead reduction is associated with a low complication rate (<1%), and a mean lowering of 1.6 cm is reported. Future studies should report mean and standard deviation of reduction, and should follow patients for at least 12 months.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".