Central sensitization inventory in endometriosis
Bibliographic record
Abstract
ABSTRACT: A key clinical problem is identifying the patient with endometriosis whose pain is complicated by central nervous system sensitization, where conventional gynecologic treatment (eg, hormonal therapy or surgery) may not completely alleviate the pain. The Central Sensitization Inventory (CSI) is a questionnaire previously validated in the chronic pain population. The objective of this study was an exploratory proof-of-concept to identify a CSI cutoff in the endometriosis population to discriminate between individuals with significant central contributors (identified by central sensitivity syndromes [CSS]) to their pain compared to those without. We analyzed a prospective data registry at a tertiary referral center for endometriosis, and included subjects aged 18 to 50 years with endometriosis who were newly or re-referred to the center in 2018. The study sample consisted of 335 subjects with a mean age of 36.0 ± 7.0 years. An increasing number of CSS was significantly correlated with dysmenorrhea, deep dyspareunia, dyschezia, and chronic pelvic pain scores (P < 0.001), and with the CSI score (0-100) (r = 0.731, P < 0.001). Receiver operating characteristic analysis indicated that a CSI cutoff of 40 had a sensitivity of 78% (95% CI: 72.7%-84.6%) and a specificity of 80% (95% CI: 70.3%-84.5%) for identifying a patient with endometriosis with ≥3 CSS. In the group with CSI ≥ 40, 18% retrospectively self-reported pain nonresponsive to hormonal therapy and 40% self-reported daily pain, compared with 6% and 20% in the CSI < 40 group (P = 0.003 and 0.002, respectively). In conclusion, a CSI ≥ 40 may be a practical tool to help identify patients with endometriosis with pain contributors related to central nervous system sensitization.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".