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Record W3094633261 · doi:10.1055/s-0040-1718943

Interdisciplinary Teams in Endometriosis Care

2020· review· en· W3094633261 on OpenAlexaff
Catherine Allaire, Alicia J. Long, Mohamed A. Bedaiwy, Paul J. Yong

Bibliographic record

VenueSeminars in Reproductive Medicine · 2020
Typereview
Languageen
FieldMedicine
TopicEndometriosis Research and Treatment
Canadian institutionsBritish Columbia Centre of Excellence for Women's HealthUniversity of British Columbia
Fundersnot available
KeywordsEndometriosisPelvic painMedicineChronic painPsychological interventionMultidisciplinary approachPhysical therapyIntensive care medicineMEDLINEPhysical examinationMultidisciplinary teamGynecologyNursingSurgery

Abstract

fetched live from OpenAlex

Endometriosis-associated chronic pelvic pain can at times be a complex problem that is resistant to standard medical and surgical therapies. Multiple comorbidities and central sensitization may be at play and must be recognized with the help of a thorough history and physical examination. If a complex pain problem is identified, most endometriosis expert reviews and guidelines recommend multidisciplinary care. However, there are no specific recommendations about what should be the components of this approach and how that type of team care should be delivered. There is evidence showing the effectiveness of specific interventions such as pain education, physical therapy, psychological therapies, and pharmacotherapies for the treatment of chronic pain. Interdisciplinary team models have been well studied and validated in other chronic pain conditions such as low back pain. The published evidence in support of interdisciplinary teams for endometriosis-associated chronic pain is more limited but appears promising. Based on the available evidence, a model for an interdisciplinary team approach for endometriosis care is outlined.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.050
GPT teacher head0.422
Teacher spread0.372 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations29
Published2020
Admission routes1
Has abstractyes

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