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Record W3163485581 · doi:10.1093/bjs/znab134.354

55 Frailty Assessment in Lower Limb Critical Limb Ischaemia Patients

2021· article· en· W3163485581 on OpenAlexaboutno aff
Madeeha Malik, John Smyth

Bibliographic record

VenueBritish journal of surgery · 2021
Typearticle
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLower limbProspective cohort studyHospital dischargeVascular surgeryEmergency medicineCritical limb ischemiaPhysical therapyVascular diseaseIntensive care medicineSurgeryArterial diseaseCardiac surgery

Abstract

fetched live from OpenAlex

Abstract Introduction An increasing number of frail older patients are undergoing surgical procedures. Frailty is an independent risk factor for increased hospital stay and adverse postoperative outcomes. This project aimed to assess frailty and its management in lower limb critical limb ischaemia patients on the vascular ward at Manchester Royal Infirmary. Method A prospective review of consecutive admissions admitted with lower limb critical limb ischaemia identified from ward list and vascular activity, and subsequently discharged. Assessment of frailty status using the Edmonton and Rockwood scales within 48 hrs of admission. Results A total of 15 patients were identified with an average age of 69.2 years. The average length of hospital stay was 19 days. 69 total days were spent in the hospital once patients were declared ‘medically fit for discharge’ across all patients. Frailty is associated with increased length of hospital stay and discharge to other institutions. Both the Edmonton and Rockwood scales were congruent in assessing frailty. Conclusions Frailty needs to be assessed and identified early so it can be flagged to therapy services and discharge arrangements commenced early. Measures need to be put in place to manage frailty and reduce the length of hospital stay.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.311
Teacher spread0.278 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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