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Record W2032758070 · doi:10.1055/s-0032-1321983

Medical and Economic Implications of Physical Disability of Survivorship

2012· review· en· W2032758070 on OpenAlexaff
John P. Kress, Margaret S. Herridge

Bibliographic record

VenueSeminars in Respiratory and Critical Care Medicine · 2012
Typereview
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineWeaknessPsychological interventionCaregiver burdenRehabilitationDistressMoodPsychiatryIntensive care medicinePhysical therapyDiseaseClinical psychologyDementia

Abstract

fetched live from OpenAlex

Interventions developed in the last decade have led to impressive rates of survival from extreme critical illness. However, surviving an episode of critical illness is just the beginning. Discharge from the intensive care unit (ICU) is often the start of a long and challenging rehabilitation, mood disorders, cognitive impairment, financial hardship, and caregiver burden, burnout, and psychological distress. It has become increasingly apparent that the majority of patients who survive an episode of critical illness will have some degree of compromised physical function secondary to ICU Acquired Weakness (ICUAW) and a constellation of other physical disabilities. The spectrum of muscle, nerve, and brain dysfunction may be permanent and can significantly change the disposition for those who were previously independent. Furthermore, it may impose a substantial health care cost burden and compromise the reserve of even the most resilient family members. Important limitations in the current literature relate to our poor understanding of how to risk stratify, how to systematically educate and inform our patients and family caregivers about physical morbidity and complex patient care in the community, and how to develop, test, and implement rehabilitation programs tailored to individual need.

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 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)0.001
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.896
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.404
Teacher spread0.343 · 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 teacher head, not a consensus.

Study designOther design
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

Citations28
Published2012
Admission routes1
Has abstractyes

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