Evaluating the Impact of the Vulvar Quality of Life Index for New Patient Dermatology Assessments
Bibliographic record
Abstract
Background: The prevalence of vulvar diseases has a significant impact on quality of life (QoL). Measurement may not be consistently collected during assessments. Incorporating a QoL measurement tool may help optimize patient-centred care. Objective: To determine the impact of the validated Vulvar Quality of Life Index (VQLI) questionnaire during consultation assessments. Methods: A randomized controlled trial from a single dermatology centre was completed over 10 months. The intervention group completed the VQLI at the baseline/initial appointment, and both the intervention and control groups completed the questionnaire at follow-up, with these scores compared. Secondary outcomes compared scores within the intervention group; analyzed treatment adherence; and surveyed self-reported symptom improvement, whether patient health-related concerns were addressed, and well-being. Data were analyzed descriptively, with significance between means and proportions assessed using t -tests and Fisher’s exact tests, respectively. Results: Forty-two patients participated. Scores within the intervention group, baseline (18) versus follow-up (8.3), were statistically significant. Follow-up VQLI scores in the intervention group (n = 23), 8.3, trended lower than the control (n = 19), 12.8, but were not statistically significant ( P = .1529). Although treatment adherence ( P = .428), symptom improvement ( P = .684), and feeling of whether health-related concerns were addressed ( P = .391) were similar, improvement in well-being ( P = .017) in the intervention group was statistically significant. Conclusions: In addition to the use of VQLI in vulvar dermatology assessments as an aid in identifying the impact of vulvar conditions on QoL, we recommend its use to improve the patients’ sense of well-being.
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.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".