Chronic pelvic pain in <scp>A</scp>ustralia and <scp>N</scp>ew <scp>Z</scp>ealand
Bibliographic record
Abstract
Chronic pelvic pain in Australia and New ZealandWith so little research into chronic pelvic pain (CPP) worldwide, current management occurs in a vacuum of knowledge few areas of medicine would accept.No statistical data for the prevalence of CPP in Australia are available, but if CPP is defined as pelvic pain on most days for more than six months, then estimates of community prevalence range from 15% (USA) 1 to 25.4% (New Zealand).2 This compares with Australian Bureau of Statistics prevalence data of 10% for asthma 3 and 14% for back pain.4 Economically, pelvic pain is estimated to cost Australia more than $6 billion annually in direct costs.5 Simoens estimates the reduced quality of life in women with endometriosis-associated symptoms treated in referral centres at 0.809 quality-adjusted life years.6 Despite this, Australia's National Women's Health Policy 7 mentions pelvic pain only in the context of chlamydial pelvic infection, and unlike asthma and musculoskeletal conditions, pelvic pain is not considered a National Health Priority Area.The complexity of the condition may be a contributing factor.While medical professionals divide the pelvis into gynaecological, gastroenterological, urological, musculoskeletal, neurological and psychological systems, the 'cross-talk' between organs and the presence of the medical condition 'chronic pain' makes these distinctions unhelpful.A woman's pain experience may include any or all of dysmenorrhoea, bladder dysfunction, irritable bowel, pelvic muscle spasm, vulvodynia, migraine headaches, fatigue, anxiety, low mood, poor sleep, premenstrual symptoms, pudendal or other peripheral neuralgias and postsurgical pain.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".