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Record W2334352458 · doi:10.4140/tcp.n.2015.38

Treating Recurrent Postmenopausal Vasomotor Symptoms in a Patient with a Positive Family History for Breast Cancer

2015· article· en· W2334352458 on OpenAlexaff
Christine Leong, Jennifer Lake

Bibliographic record

VenueThe Consultant Pharmacist · 2015
Typearticle
Languageen
FieldMedicine
TopicMenopause: Health Impacts and Treatments
Canadian institutionsUniversity of ManitobaApotex (Canada)
Fundersnot available
KeywordsMedicineGabapentinBreast cancerFamily historyBedtimeVasomotorHormone replacement therapy (female-to-male)CancerClinical trialInternal medicinePediatricsTestosterone (patch)

Abstract

fetched live from OpenAlex

OBJECTIVE: To report a case of recurrent hot flashes unresponsive to gabapentin in a postmenopausal patient with a positive family history of breast cancer. CASE SUMMARY: A 69-year-old Caucasian female experienced a recurrence of debilitating hot flashes for the past eight months. More recently, she failed a two-month trial of gabapentin 600 mg by mouth at bedtime after she previously received effective hormone replacement therapy (HRT) seven years ago with near-complete resolution of her symptoms. The patient had a sister and a niece who developed breast cancer in their 40s. DISCUSSION: The treatment of postmenopausal hot flashes in a patient with a positive family history of breast cancer represents a clinical challenge for many clinicians. This case is an example in which gabapentin was ineffective in the treatment of severe hot flashes in a postmenopausal woman. The risks and benefits of HRT compared with nonhormonal alternatives were assessed. CONCLUSION: In this case, a two-month trial of gabapentin 600 mg/day failed to demonstrate efficacy in reducing the severity, frequency, and duration of hot flashes. Controlled trials are necessary to evaluate the safety and efficacy of other therapeutic alternatives.

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)0.000
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: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.061
GPT teacher head0.344
Teacher spread0.283 · 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 designRandomized trial
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
Published2015
Admission routes1
Has abstractyes

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