Global burden and trends of pelvic inflammatory disease associated with sexually transmitted infection excluding HIV from 1990 to 2021
Bibliographic record
Abstract
Background: Pelvic inflammatory disease (PID) is mainly induced by the sexually transmitted infection (STI). However, the global burden and trends of STI excluding human immunodeficiency virus (HIV)-associated PID (non-HIV PID) has not been specifically assessed. Methods: The prevalence and years lived with disability (YLDs) were collected from the Global Burden of Disease (GBD) 2021 database. The disease burden was evaluated with the case numbers, age-standardized rates (ASR) and estimated annual percentage changes (EAPC). According to the SocioDemographic Index (SDI), frontier and health inequality analysis were conducted. Autoregressive Integrated Moving Average (ARIMA) model was applied to predict the future trends of Non-HIV PID. Results: The age-standardized prevalence rates (ASPR) and YLDs of non-HIV PID was 27.02/100,000 and 3.68/100,000 in 2021 globally. Except for the decline of gonococcal-associated PID, the EAPC of chlamydial and other non-HIV PID were stable. The countries with fastest-growing prevalence were Brazil (4.19 [2.92, 5.47]), Spain (3.98 [3.19, 4.77]), Greece (3.05 [2.55, 3.55]), Portugal (2.76 [2.22, 3.29]), which suggested the increased burden of non-HIV PID in these years. Moreover, the non-HIV PID was mainly concentrated in 30-34 years, which was most common in the low and low-middle SDI. Additionally, prevention of non-HIV PID should also be concerned in the high SDI regions, especially for United Kingdom, Canada, Japan, and Singapore, which would also increase in the next 30 years. Conclusion: The burden and prevention of non-HIV PID were still arduous and required a long-term effort, especially for the 30-34 years, which need more attentions even for the developed countries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".