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Record W4414372058 · doi:10.1097/phm.0000000000002778

Seven-Year Intervention Rate and Effect of Early Rehabilitation in China

2025· article· en· W4414372058 on OpenAlexaff
Yuanmingfei Zhang, Hua Zhang, Ming’ai Zhou, Siyan Zhan, Yanyan Yang, H.B. Wang, Ying Shi, Lanxia Gan

Bibliographic record

VenueAmerican Journal of Physical Medicine & Rehabilitation · 2025
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsRehabilitationIntervention (counseling)Psychological interventionMedical recordSpinal cord injuryStroke (engine)

Abstract

fetched live from OpenAlex

ABSTRACT: The improvement of early rehabilitation service capacity is of great importance for meeting health needs of the population; however, there is still a lack of information on early rehabilitation interventions in hospitals in China. This study selected the first page data of 7,914,692 medical records suffered the stroke and brain injury, spinal cord injury, spinal and joint degeneration, fracture and sports injury from 1305 tertiary general hospitals in China from 2016 to 2022. We found that the rate of early rehabilitation intervention in tertiary general hospitals in China increased annually from 2016 to 2022, especially in 2020. The rate of early rehabilitation intervention in patients with complications was higher than that in patients without complications at 7 years. Regardless of complications, the mortality rate of patients undergoing early rehabilitation was lower than that of patients without early rehabilitation. However, the length of hospital stay and total cost were higher in patients undergoing early rehabilitation than those without early rehabilitation. The change in this indicator varied from province to province. This study combined quality improvement measures to summarize the experience of improving the early rehabilitation intervention rate in less developed countries.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.002
GPT teacher head0.292
Teacher spread0.290 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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