The Most Influential Publications Regarding Hair Transplantation: A Bibliometric Review
Bibliographic record
Abstract
INTRODUCTION: This bibliometric review aims to assess the impact of significant publications within the field of hair transplantation. Citation counts will serve as a primary influence indicator. METHODS: An exhaustive search was conducted using Clarivate's Web of Science database, yielding 260 publications related to hair transplantation. These were evaluated and sorted based on citations, narrowing down to the 50 most highly cited works for analysis. Parameters including citation density, authorship, institutional affiliations, country of origin, year of publication, article topic, and the level of evidence for each publication were obtained. RESULTS: Analyzed publications were cited a total of 1341 times. Authorship analysis revealed that the most significant contributors regarding hair transplantation were Bernstein and Rassman. We also identified the leading institutions affiliated with these works, highlighting the primary academic and research centers driving the field. Geographical analysis exhibited the US' dominance in producing impactful publications. Most publications were also classified within Level IV and Level V according to the Oxford Levels of Evidence system. CONCLUSION: This review provides a comprehensive snapshot of the pivotal publications shaping hair transplantation. Our findings underscore significant contributions within this field and may assist researchers and clinicians in understanding the evolution and the current state of the hair transplantation literature. This bibliometric analysis can serve as a roadmap for those seeking to delve into this rapidly evolving field, facilitating the identification of research gaps and formulating future research directions. LEVEL OF EVIDENCE V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
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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 | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.013 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.163 | 0.164 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".