Endometriosis and chronic pelvic pain management: Between a rock (denial) and a hard place (overdiagnosis)
Bibliographic record
Abstract
Endometriosis and chronic pelvic pain management: Between a rock (denial) and a hard place (overdiagnosis) Sir, In their thought-provoking commentary, Hudelist et al. 1 highlightedthe inconsistent association between endometriosis and chronic pelvic pain.They warned that the origin of symptoms may be multifactorial and not always due to the presence of ectopic endometrium.For the inexperienced clinician, it could be easier to attribute pain to endometriosis than to delve into a challenging differential diagnosis that includes several potential causes, some of which may be unfamiliar.The authors emphasize the risk of misdiagnosis, especially in patients with noncyclic pelvic pain and with superficial peritoneal endometriosis, as well as the risk of overdiagnosis of adenomyosis.
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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 | 0.014 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.030 | 0.036 |
| Insufficient payload (model declined to judge) | 0.004 | 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".