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Record W4395072251 · doi:10.54097/xgstf920

Visual Analysis of Geriatric Depression Treatment Based on CiteSpace

2024· article· en· W4395072251 on OpenAlexaff
Yiming Zang

Bibliographic record

VenueJournal of Education Humanities and Social Sciences · 2024
Typearticle
Languageen
FieldMedicine
TopicMedical Research and Treatments
Canadian institutionsUniversity of British Columbia, Okanagan CampusKelowna General Hospital
Fundersnot available
KeywordsDepression (economics)PsychologyEconomics

Abstract

fetched live from OpenAlex

CiteSpace software was used to conduct scientometric analysis on the research and intervention measures of geriatric depression (GD) at home and abroad, revealing the research hotspots, evolution process and development trend of Chinese and Western medicine. It was found that the United States remains absolutely dominant, regardless of the breadth of the research area, the number of publications, or the influence. Western medicine has focused its recent research on mortality in a number of domains, including instrumental activity, cognitive assessment, international society, and treatment response. Chinese medicine, on the other hand, focuses on investigating the pathophysiology and holistic treatments of depression in older adults. Traditional Chinese medicine (TCM) treatment of geriatric depression exhibits the qualities of minimal toxicity and side effects, minimal or no trauma, and evident clinical results. As a global public health problem, the active promotion of inter-country cooperation and joint interdisciplinary interventions is the development trend.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.998
Threshold uncertainty score0.195

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0170.012
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0580.004

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 head0.086
GPT teacher head0.452
Teacher spread0.367 · 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.

Study designNot applicable
DomainEvaluation
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
Published2024
Admission routes1
Has abstractyes

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