Forehead Reduction: A Systematic Review and Meta-Analysis of Outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.032 | 0.005 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it