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Record W2127488254 · doi:10.1177/0884533614545997

Low Vitamin B<sub>12</sub> in Patients on Admission to an Amputation Rehabilitation Unit

2014· article· en· W2127488254 on OpenAlexaff
Eric Earl, Renee Wong, Michael W. Payne

Bibliographic record

VenueNutrition in Clinical Practice · 2014
Typearticle
Languageen
FieldMedicine
TopicFolate and B Vitamins Research
Canadian institutionsSt Joseph's Health CareWestern University
Fundersnot available
KeywordsMedicineAmputationRehabilitationMedical recordAnemiaMean corpuscular volumePopulationRetrospective cohort studyEtiologyPhysical therapyPediatricsInternal medicineHematocritSurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study was to determine the prevalence of low vitamin B12 (VB12) in patients on admission to an amputation rehabilitation unit and identify specific populations at risk. METHODS: A retrospective chart review was performed for 127 participants comprising patients with major lower limb amputations admitted to a regional amputation rehabilitation program between January 1, 2011 and December 31, 2012. Electronic medical records were reviewed for demographic data, amputation data, medication history, serum VB12 levels, and other related blood work. A literature-based cutoff of VB12 <260 pmol/L was used as the criterion for low VB12. RESULTS: The prevalence of low VB12 was 59.8%. Patients aged ≥55 years were found to have an increased prevalence of low VB12 (P = .05). Serum VB12 levels were significantly lower among patients aged ≥55 years (P < .05) and among patients with a mean corpuscular volume >97 fL (P < .01). No other differences in prevalence were determined among different demographics, etiologies, or comorbidities. CONCLUSIONS: Patients with an amputation have a high prevalence of low VB12 levels compared with the general population. Low VB12 status may impact rehabilitation outcomes through anemia, cognitive decline, and neuropathy. No reliable indicators for which patients should be screened were found, and therefore, a universal approach to screening and treatment is needed.

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)0.004
metaresearch head score (Gemma)0.043
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.055
GPT teacher head0.445
Teacher spread0.390 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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