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Record W3120993426 · doi:10.5539/gjhs.v13n2p118

Traditional Chinese Medicine for Mammary Dysplasia (Fibrocystic Breast Disease): A Case Report

2021· article· en· W3120993426 on OpenAlexvenueno aff
Abeer Elmohandes

Bibliographic record

VenueGlobal Journal of Health Science · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHedgehog Signaling Pathway Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFibrocystic Breast DiseaseBreast cancerAcupunctureDiseaseTraditional Chinese medicineAlternative medicineFibrocystic diseaseFeelingTraditional medicineCancerGynecologyDermatologyInternal medicinePathologyPsychology

Abstract

fetched live from OpenAlex

Breast cancer (BC) is one among the most common and fatal malignancies affecting women of all ages, though prognosis are good if caught early. Fibrocystic breast disease, which is interchangeably mentioned as fibrocystic breast, is nothing but a benign (noncancerous) condition which gives a lumpy feeling to women. It is not a rapidly progressing disease or dangerous, but is a constant source of problem to some women. In this case report, we present a female suffering from mammary dysplasia (MD). She was initially treated with conventional medicine but received no benefit, so shifted to Traditional Chinese medicine (TCM). Here, acupuncture, herbal remedies, and specific instructions for a dietary regimen and bra usage were given. We found that by utilizing this holistic approach to treat the root cause, rather than the symptoms, breast pain was eliminated, cystic lumps and related densities were diminished, and cancer progression was thwarted by pursuing healthy lifestyle modification and paying more attention to diet and exercise. Ultimately, this resulted in a better quality of life and suggests that TCM can be employed successfully to treat MD when conventional medicine has failed.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.003
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0070.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.027
GPT teacher head0.335
Teacher spread0.308 · 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 designCase report
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

Citations0
Published2021
Admission routes1
Has abstractyes

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Same venueGlobal Journal of Health ScienceSame topicHedgehog Signaling Pathway StudiesFrench-language works237,207