A Scoping Review of Treatment Outcome Measures for Vulvar Intraepithelial Neoplasia
Bibliographic record
Abstract
OBJECTIVE: The goal of this study is to identify a list of clinician-reported outcome measures (CROMs) and patient-reported outcome measures (PROMs) through a review of published studies reporting on any therapeutic interventions for vulvar intraepithelial neoplasia (VIN). MATERIALS AND METHODS: A systematic search of published studies reporting on any therapeutic interventions for VIN was performed on MEDLINE, Embase, Cochrane Database, PsychInfo, and CINAHL from inception to September 20, 2021, based on predetermined study selection criteria. Data were extracted and analyzed by 2 authors independently using Covidence software. RESULTS: Thirty two of 2386 studies identified met study selection criteria. None of the 32 studies provided an explicit definition of VIN treatment "success." The most common CROM was "clinical response to treatment." The most common scale used to measure this outcome was "complete response/partial response/no response"; however, 17 of 23 studies (73.9%) did not define these values. Laboratory CROMs were reported in 12/32 (37.5%) studies. Patient-reported outcome measures were reported in only 10 of 32 studies(31.3%) -the most common PROM was "symptoms." Only 2 of 32 studies measured PROMs related to "quality of life" domains. Adverse events/treatment-related adverse effects were reported in 24 of 32 studies (75%), although 71% of studies provided no details on how these data were collected. CONCLUSIONS: There is a large variation in outcome measures, instruments, and scales used for any clinician-reported treatment outcome such as "clinical response." Most studies do not include patient-reported outcome measures assessing quality of life domains. A Core Outcome Set for the treatment of VIN is needed to improve the quality of VIN research.
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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 | 0.032 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.032 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".