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Record W4214745062 · doi:10.5455/ovj.2022.v12.i1.13

The relationship between urethral sphincter mechanism incompetency and lower back pain: positing a novel treatment for urinary incontinence in dogs

2022· article· en· W4214745062 on OpenAlexaff

Bibliographic record

VenueOpen Veterinary Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsRoyal Military College of CanadaSquamish Nation
Fundersnot available
KeywordsUrethral sphincterSphincterUrinary incontinenceMechanism (biology)Reduction (mathematics)Lumbosacral plexus

Abstract

fetched live from OpenAlex

Background: In humans, multiple researchers have not only determined that there is a relationship between urinary incontinence (UI) and lower back pain (LBP), but that by treating the LBP, clinicians are able to improve or resolve the UI. Up until now, no equivalent canine research has investigated whether treatment of LBP can improve the clinical signs of acquired, non-neurologic UI in dogs. Aim: To determine if a relationship exists between LBP and urethral sphincter mechanism incompetence (USMI) in dogs. Methods: Review of medical records of all patients that presented to Points East West Veterinary Services with a history of naturally occurring acquired UI from May 2013 to December 2019. Thirty-nine patients treated for LBP using combined acupuncture and manual therapy, and 33/39 patients that also received concurrent photobiomodulation (PBM) therapy, qualified for this study. Results: < 0.01) of UI episodes. Treatment responses ranged from no improvement, to complete resolution of the USMI clinical signs. Conclusion: The reduction of USMI clinical signs following LBP treatment suggests a relationship between these two conditions. Combined acupuncture, manual therapy, with or without PBM was shown to be an effective treatment for USMI. By corollary, USMI incontinence should be considered a potential pain symptom.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.087
GPT teacher head0.338
Teacher spread0.252 · 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 designObservational
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

Citations2
Published2022
Admission routes1
Has abstractyes

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