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Record W4313543996 · doi:10.29271/jcpsp.2023.01.107

Late Presentation of Acute Limb Ischemia: Causes and Outcomes

2023· article· en· W4313543996 on OpenAlexaff
Munazza Saleem

Bibliographic record

VenueJournal of College of Physicians And Surgeons Pakistan · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsAthabasca University
Fundersnot available
KeywordsMedicineAdverse effectPatient safetyMedication errorHarmIntensive care medicineNear missDrug administrationFood and drug administrationBarcodeMedical emergencyPharmacologyHealth careComputer sciencePsychologyEngineering

Abstract

fetched live from OpenAlex

Objective: To determine the causes of interventions in patients with acute limb ischemia (ALI), the time lapse of presentation, and the outcome.Study Design: An observational study.Methodology: All delayed acute limb ischemia cases (presenting later than 6 hours after the onset of symptoms) were included in the study.Cases of ALI secondary to accidental trauma were excluded except those of iatrogenic trauma like patients with intravenous drug abuse and intra-arterial accidental drug infiltration.Patients' demographic data, clinical history, aetiology, examination findings, and treatment data; including type of surgery, level of amputation, adjunctive treatment were recorded.Results: Total number of delayed ALI cases was 147.Mean age was 59.52 17.77 years.Seventy-five (51%) were females while 72(49%) were males.The right lower limb was involved in 56(38%) cases.A hundred (68%) thromboembolectomies were successful and limbs were saved, while 19(12.9%)had failure after the procedure.Three (2%) patients expired within 24 hours of thromboembolectomy.Twenty-five (17%) had frank gangrene at presentation and ended up in amputations while 122 (82.9%) had questionable viability and underwent limb salvage procedures.Conclusion: Delayed presentation of ALI is very common; timely management with effective thromboembolectomies can save limbs in most of the patients.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.067
Threshold uncertainty score0.279

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.032
GPT teacher head0.396
Teacher spread0.364 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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