<p>Rethinking endometriosis care: applying the chronic care model via a multidisciplinary program for the care of women with endometriosis</p>
Bibliographic record
Abstract
Endometriosis is a chronic, painful disease without a cure. Due largely to chronic pain, endometriosis can lead to significant physical, mental, relationship, and financial burdens. Within the conventional single provider model of care-in which the patient is primarily taken care of by her physician and complementary strategies based on psychology, nutrition, pain medicine, pelvic physical therapy, and so on may not be readily available in a coordinated manner-most women with endometriosis live with unresolved pain and the consequences of that pain. We therefore propose that there is an urgent need to search for alternative models of care. In the current paper, we discuss our experiences with an model of care in which we adopt a long-term, patient-focused, and multidisciplinary chronic care model for women with endometriosis. Our objective is to improve long-term clinical outcomes for women with endometriosis. For geographical areas and healthcare systems in which it is feasible, we propose consideration of this multidisciplinary model of care as an alternative to the single provider model and offer guidance for those considering establishment of such a program. We also initiate a conversation about which clinical outcomes pertaining to endometriosis are important and should be tracked to assess the efficacy and value of multidisciplinary and other endometriosis healthcare models.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".