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Data_Sheet_2_Tracing the evolving dynamics and research hotspots of spinal cord injury and surgical decompression from 1975 to 2024: a bibliometric analysis.XLSX

2024· dataset· en· W6946221539 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2024
Typedataset
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
Fundersnot available
KeywordsSpinal cord injuryDecompressionWindow of opportunitySurgical decompressionWeb of scienceSpinal cordBibliometrics

Abstract

fetched live from OpenAlex

Background Exploration of the benefits and timing of surgical decompression in spinal cord injury (SCI) has been a research hotspot. However, despite the higher volume and increasing emphasis on quality there remains no bibliometric view on SCI and surgical decompression. In this study, we aimed to perform bibliometric analysis to reveal the core countries, affiliations, journals, authors, and developmental trends in SCI and surgical decompression across the past 50 years. Methods Articles and reviews were retrieved from web of science core collection between 1975 and 2024. The bibliometrix package in R was used for data analysis and visualizing. Results A total of 8,688 documents were investigated, indicating an ascending trend in annual publications. The USA and China played as the leaders in scientific productivity. The University of Toronto led in institutional productions. Core authors, such as Michael G. Fehlings, showed high productivity, and occasional authors showed widespread interests. Core journals like Spine and Spinal Cord served as beacons in this field. The interaction of core authors and international collaboration accentuated the cross-disciplinary feature of the field. Prominent documents emphasized the clinical significance of early decompression in 24 h post SCI. Conclusion Based on comprehensive bibliometric analysis and literature review, we identified the hotspots and future directions of this field: (1) further investigation into the molecular and cellular mechanisms to provide pre-clinical evidence for biological effects of early surgical decompression in SCI animal models; (2) further evaluation and validation of the optimal time window of surgical decompression based on large cohort, considering the inherent heterogeneity of subpopulations in complicated immune responses post SCI; (3) further exploration on the benefits of early decompression on the neurological, functional, and clinical outcomes in acute SCI; (4) evaluation of the optimal surgical methods and related outcomes; (5) applications of artificial intelligence-based technologies in spinal surgical decompression.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaBibliometrics
Domain: not available · Genre: Dataset
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptInsufficient payload (model declined to judge)
Domain: not available · Genre: Dataset
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.573
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.048
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.5760.003

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 head0.096
GPT teacher head0.395
Teacher spread0.299 · 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

Labeled directly by 2 models reading the full record.

BibliometricsInsufficient payload (model declined to judge)

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable
Domainnot available
GenreDataset

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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