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Record W4200555000 · doi:10.3390/diagnostics12010019

Components of the Endogenous Cannabinoid System as Potential Biomarkers for Interstitial Cystitis/Bladder Pain Syndrome

2021· review· en· W4200555000 on OpenAlexaff
Saki Sultana, Geraint Berger, Christine Lehmann

Bibliographic record

VenueDiagnostics · 2021
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsInterstitial cystitisBladder Pain SyndromeMedicineEtiologyPelvic painUrinary systemEndogenyInflammationChronic painEndocannabinoid systemCannabinoidInternal medicineBioinformaticsPhysical therapySurgeryBiologyReceptor

Abstract

fetched live from OpenAlex

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a chronic condition causing bladder pressure and pain. The condition is of unknown etiology and is often accompanied by other symptoms, including chronic pelvic pain, increased urinary urgency, and frequency. There is no definitive diagnosis for IC/BPS, and treatment options are currently limited to physical therapy and medications to help alleviate symptoms. The endogenous cannabinoid system (ECS) is an important regulator of numerous physiological systems, including the urinary system. Modulations of the ECS have been shown to be beneficial for IC/BPS-associated pain and inflammation in rodents. As an attempt to identify potential biomarkers for IC/BPS, we reviewed experimental studies where the components of the ECS have been quantified in experimental models of IC/BPS. Further investigations using well-defined animal models and patients' data are required to obtain stronger evidence regarding the potential for ECS components to be definitive biomarkers for IC/BPS.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.650
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.045
GPT teacher head0.314
Teacher spread0.270 · 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.

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

Citations9
Published2021
Admission routes1
Has abstractyes

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