The Importance of Patient-Reported Outcome Measures (PROMs) in Oncological Vulvoperineal Defect Reconstruction: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcome measures (PROMs) have gained increased importance in assessing outcomes after reconstructive surgery. This also applies to the reconstruction of vulvoperineal defects after resection of gynecological or colorectal cancers in women. The objective of this study is to analyze the current state of PROM tool use within this patient population. METHODS: By systematic literature searches in Embase, Medline, and Web of Science, English-language studies published after 1980, including randomized controlled trials, cohort studies, and case series reporting on vulvoperineal defect reconstruction, which were included if they also analyzed quality of life (QoL) and/or PROMs. The PROM tools used by each study were extracted, analyzed, and compared. RESULTS: The primary search yielded 2576 abstracts, of which 395 articles were retrieved in full text. Of these, 50 reported on vulvoperineal defect reconstruction, among which 27 studies analyzing QoL were found. Of those, 17 met the inclusion criteria for this systematic review. After full-text screening, 14 different PROM tools and 5 individual, non-standardized questionnaires were identified. Only 22% of studies used a validated PROM tool. CONCLUSION: Far too few studies currently use PROM tools to assess outcomes in oncological vulvoperineal defect reconstruction. Less than half of the used PROMs are validated. No PROM was designed to specifically measure QoL in this patient population. The standardized implementation of a validated PROM tool in the clinical treatment of this patient population is an essential step to improve outcomes, enable the comparison of research, and support evidence-based treatment approaches.
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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 | 0.038 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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 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".