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Record W2136313684 · doi:10.1111/pme.12625

Reduction of Pain Sensitivity is Associated with the Response to Treatment in Women with Chronic Pelvic Pain

2014· article· en· W2136313684 on OpenAlexaboutno aff
Maria Beatriz Cardoso Ferreira, Omero Benedicto Poli‐Neto, Julio Cesar Rosa e Silva, Antônio Alberto Nogueira, Francisco José Cândido dos Reis

Bibliographic record

VenuePain Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicEndometriosis Research and Treatment
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMedicineVisual analogue scalePelvic painMcGill Pain QuestionnaireTranscutaneous electrical nerve stimulationChronic painAnesthesiaThreshold of painPhysical therapySensitizationSurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this study was to evaluate whether pain thresholds to electrical stimulation of the skin change in the response to treatment in women with chronic pelvic pain (CPP). METHODS: Fifty-eight women with persistent pelvic pain for at least 6 months, from a tertiary care setting, were included in this study. All women were evaluated before the therapeutic intervention and at 6 months of multidisciplinary treatment. To estimate the pain threshold, we used transcutaneous electrical nerve stimulation on the anterior surface of the nondominant arm. The intensity of clinical pain was estimated by a visual analog scale and by the McGill questionnaire. RESULTS: The mean of pain threshold increased from 14.2 to 17.4 after 6 months of treatment (P < 0.0001). The effect sizes of the increase of electrical pain threshold were 0.86 (95% CI, 0.38 to 1.34) in the group with pain reduction and 0.53 (95% CI, -0.08 to 1.15) in the group without pain reduction. CONCLUSION: The sensitivity to experimental pain was reduced after 6 months of multidisciplinary treatment for CPP. Our data provided additional evidence of central sensitization in women with CPP.

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)0.028
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0280.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.013
GPT teacher head0.282
Teacher spread0.269 · 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 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

Citations12
Published2014
Admission routes1
Has abstractyes

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