The Quality and Quantity of Lower Genital Tract Research Across Multiple Journals
Bibliographic record
Abstract
OBJECTIVE: This study aimed to determine the quantity and quality of lower genital tract disease (LGTD) research by topic published across a variety of gynecology and dermatology journals. METHODS: Authors accessed all articles that were rejected (1,111, 59.5%) and accepted (755, 40.5%) by the Journal of Lower Genital Tract Disease ( JLGTD ) from 2008 to 2020. Studies were categorized by key topic: Cervix, Human Papillomavirus, Vulva, Vagina, Anal, and Other. Studies were further subcategorized based on methodology. These data were compared with all LGTD publications from 2018 to 2020 in 4 other widely recognized journals ( Obstetrics and Gynecology , The British Journal of Obstetrics and Gynaecology , JAMA Dermatology , and the British Journal of Dermatology ). RESULTS: Most JLGTD -accepted submissions were related to the cervix (298/755, 39.5%) and vulva (189/755, 25.0%). Rates of acceptance were similar across all key topic areas. Only 3.2% of publications in the other 4 journals (92/2,932) were related to LGTD topics. Across all 5 journals, vulva studies were most commonly case reports/case series (82/218, 37.6%), with a low prevalence of systematic reviews/meta-analyses (4/218 1.8%). In comparison, cervix studies had the highest number of systematic reviews/meta-analyses (14/317, 4.4%) and the lowest number of case reports (14/317, 4.4%). CONCLUSIONS: Vulvar research is of lower quality compared with cervix research published across 5 journals. Comparing accepted versus rejected articles in JLGTD , there is no publication bias against vulva topics noted; rather, the overall research quality in vulva is lower than that of cervical disease. This is a call to action for higher quality vulvar 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 | 0.183 | 0.534 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.059 | 0.055 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".