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Record W2068227953 · doi:10.1159/000067137

Aging and Stroke Rehabilitation

2003· article· en· W2068227953 on OpenAlexaboutno aff
Stefano Paolucci, Gabriella Antonucci, Elio Troisi, Maura Bragoni, Paola Coiro, Domenico De Angelis, Luca Pratesi, Vincenzo Venturiero, Maria Grazia Grasso

Bibliographic record

VenueCerebrovascular Diseases · 2003
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineStroke (engine)RehabilitationPhysical medicine and rehabilitationPhysical therapy

Abstract

fetched live from OpenAlex

The aim of this study was to assess the specific influence of age on basal functional status and rehabilitation results. We conducted a case-comparison study on 150 stroke inpatients. They were enrolled in homogeneous subgroups, matched for severity of stroke (measured by Canadian Neurological Scale - CNS) and onset admission interval (within 3 days) and divided into five subgroups according to age: or=85 years. Even when severity of stroke was the same, increasing age was associated with greater disability in activities of daily living (ADL) and mobility, minor results of rehabilitation treatment and shorter length of stay. Patients >or=85 years were nearly ten times as likely to show a low response in ADL (OR = 9.28, 95% CI = 2.89-29.76) and nearly six times in mobility (OR = 6.13, 95% CI = 2.18-17.25) than younger patients. However, rehabilitation treatment was efficacious also in patients >or=85 years, with effectiveness of treatment 27.96% on ADL and 18.64% on mobility. On one hand our results confirm the unfavorable influence of age on functional outcome and on the other that inpatient rehabilitation is substantially effective also for very old patients, although less than for younger ones.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.242
Teacher spread0.236 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations63
Published2003
Admission routes1
Has abstractyes

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