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Record W2471676595 · doi:10.3233/bsi-160149

Integration of spectroscopy and imaging to optimize evaluation of voiding dysfunction

2016· article· en· W2471676595 on OpenAlexaff
Lynn Stothers, Andrew Macnab

Bibliographic record

VenueBiomedical Spectroscopy and Imaging · 2016
Typearticle
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
Fundersnot available
KeywordsPelvic floorModalitiesIn vivo magnetic resonance spectroscopyMedicinePelvic floor dysfunctionStructural integrityIntensive care medicineMedical physicsRadiologyMagnetic resonance imagingSurgeryEngineering

Abstract

fetched live from OpenAlex

Voiding dysfunction occurs due to the interplay of anatomic, physiologic and functional elements. Hence, integration of new imaging and spectroscopy modalities offers the potential for improving patient assessment by enabling the causal structural defects, formal staging of pelvic floor dysfunction and underlying physiologic mechanisms to be better defined. The purpose of this review is to outline the limitations of current imaging, and highlight the advantages of newer technologies in the evaluation of patients with voiding dysfunction due to loss of structural integrity of the pelvic floor.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.311
Teacher spread0.295 · 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 designBench or experimental
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

Citations1
Published2016
Admission routes1
Has abstractyes

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