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Complexity of the Relation Between Physical Activity and Stroke: A Meta-Analysis

2005· article· en· W1984256082 on OpenAlexaff
Wieslaw Oczkowski

Bibliographic record

VenueClinical Journal of Sport Medicine · 2005
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineStroke (engine)Cohort studyPhysical therapyRelative riskPhysical activityCohortMeta-analysisInternal medicineConfidence interval

Abstract

fetched live from OpenAlex

OBJECTIVE: To explore the associations of recreational and occupational physical activity with ischemic and hemorrhagic stroke by means of meta-analysis. DATA SOURCES: PUBMED was searched for studies published in English up to December 2001. Keywords used were physical activity, exercise, cerebrovascular disease, stroke, ischaemic stroke, and haemorrhagic stroke. Reference lists were reviewed for additional studies. STUDY SELECTION: Cohort and case-control studies investigating the relation between physical activity and stroke were identified (n = 36). The article reporting the longest follow-up time was included if there was > or =1 report of the same study (4 exclusions). Studies had to report a measure of physical activity. A study that measured physical fitness was excluded. DATA EXTRACTION: Details of study methods, samples, levels of physical activity, types of stroke, and estimates of risk (relative risk, RR) were extracted independently by 3 reviewers from the 24 cohort and 7 case-control studies that were included. Authors of relevant studies were contacted to obtain missing data. Risk estimates were standardized. Risks for subgroups within the samples (e.g., women and men) were treated as separate study units. Physical activity was separated into occupational and leisure activity. MAIN RESULTS: In studies of occupational activity, risk of hemorrhagic stroke (RR, 0.31; 95% CI, 0.13-0.76; 1 study) and risk of ischemic stroke (RR, 0.57; CI, 0.43-0.77; 5 studies) were lower for people most active versus inactive at work, risk of ischemic stroke was lower for people who were most active versus moderately active (RR, 0.77; CI, 0.60-0.98; 5 studies), and risk of total stroke was lower for people moderately active versus inactive (RR, 0.64; CI, 0.48-0.87; 4 studies), but none of the other 8 comparisons of level of occupational activity showed significant differences. In studies of leisure-time activity, risk of total stroke (RR, 0.78; CI, 0.71-0.85; 19 studies), risk of hemorrhagic stroke (RR, 0.74; CI, 0.57-0.96, 9 studies), and risk of ischemic stroke (RR, 0.79; CI, 0.69-0.91; 11 studies) were lower for people most active versus inactive. Risk of total stroke was lower for people moderately active versus inactive (RR, 0.85; CI, 0.78-0.93; 15 studies), but none of the other 7 comparisons showed significant differences. In regression analysis weighted by methodologic quality, RR of stroke for active persons was slightly lower, with narrower confidence intervals. Leisure-time activity compared with inactivity was associated with a greater reduction in risk of hemorrhagic stroke in men (RR, 0.54; CI, 0.36-0.81) than in women (RR, 0.76; CI, 0.67-0.86). Studies conducted in Europe found more effect of activity (RR, 0.47; CI, 0.33-0.66; 3 studies) than US studies (RR, 0.82; CI, 0.75-0.90). Study results did not differ by type of study or year of publication. CONCLUSIONS: Recreational and occupational physical activity were both associated with reductions in risk of stroke. Studies varied widely in their estimates of risk; lower risks were found for active versus inactive men than women and for European versus US studies.

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)0.049
metaresearch head score (Gemma)0.099
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.261

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.099
Meta-epidemiology (narrow)0.0050.003
Meta-epidemiology (broad)0.0280.076
Bibliometrics0.0090.008
Science and technology studies0.0010.001
Scholarly communication0.0060.003
Open science0.0040.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.222
GPT teacher head0.446
Teacher spread0.224 · 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 designMeta-analysis
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

Citations11
Published2005
Admission routes1
Has abstractyes

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