Citation Analysis in Breast Reconstruction Publications Between 2000 and 2010
Bibliographic record
Abstract
Introduction and Purpose: Breast reconstruction is an active area of plastic surgery research. Citation analysis allows for quantitative analysis of publications, with more citations presumed to indicate greater influence. We performed citation analysis to evaluate the most cited papers on breast reconstruction between 2000 to 2010 to identify contemporary research trends. Methods: The SCI-EXPANDED database was used to identify the 50 most cited papers. Data points included authorship, publication year, publication journal, study design, level of evidence, number of surgeons/institutions, center of surgery, primary outcome assessed, implant/flap/acellular dermal matrix/fat graft, acellular dermal matrix brand and use with implants/flaps, fat graft use with implants/flaps, unilateral/bilateral, one-/two-stage, immediate/delayed, number of patients/procedures, complications. Descriptive analysis of trends was performed based on results. Results: 20% of papers were published in 2006, 16% in 2007 and 12% in both 2004/2009. 66% were published in Plastic and Reconstructive Surgery. The majority were retrospective or case series, and of Level III or IV evidence. The one Level I study was a prospective multicenter trial. 21 and 7 papers discussed procedures by single/multiple surgeons, respectively. Results from single/multiple centers were discussed in 18 and 6 papers, respectively. 30 papers discussed implant-based reconstruction, 22 papers flap-based (19 microsurgical), 15 papers acellular dermal matrix, and five papers fat grafting. The primary focus in the majority was complications or outcomes. Conclusion: Our analysis demonstrates continually evolving techniques in breast reconstruction. However, there is notable lack of high quality evidence to guide surgical decision-making in the face of increasing surgical options.
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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 | 0.012 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.121 | 0.169 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".