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Record W2962704913 · doi:10.2147/ijwh.s207373

<p>Rethinking endometriosis care: applying the chronic care model via a multidisciplinary program for the care of women with endometriosis</p>

2019· article· en· W2962704913 on OpenAlexaff
Sanjay K. Agarwal, Lauren Foster, Erik J. Groessl

Bibliographic record

VenueInternational Journal of Women s Health · 2019
Typearticle
Languageen
FieldMedicine
TopicEndometriosis Research and Treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEndometriosisMultidisciplinary approachMedicineGynecologyPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.828
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.348
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations66
Published2019
Admission routes1
Has abstractyes

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