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Record W1847700731 · doi:10.1111/hdi.12088

Symptomatic hyperammonemia after lung transplantation: Lessons learnt

2013· article· en· W1847700731 on OpenAlexvenueno aff
Siddiq Anwar, Diptesh Gupta, Muhammad Aleem Ashraf, Syed Arsalan Khalid, Syed Rizvi, Brent W. Miller, Daniel C. Brennan

Bibliographic record

VenueHemodialysis International · 2013
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsHyperammonemiaMedicineDiscontinuationDialysisTransplantationHemodialysisLung transplantationUrea cycleComplicationIntensive care medicineInternal medicineSurgery

Abstract

fetched live from OpenAlex

Hyperammonemia, post-orthotopic lung transplantation, is a rare but mostly fatal complication. Various therapies, including those to decrease ammonia generation, increase nitrogen excretion, and several dialytic methods for removing ammonia have been tried. We describe three lung transplant recipients who developed acute hyperammonemia early after transplantation. Two of the three patients survived after a multidisciplinary approach including discontinuation of drugs, which impair urea cycle, aggressive ammonia reduction with prolonged daily intermittent hemodialysis (HD), and overnight slow low-efficiency dialysis in conjunction with early weaning of steroids and other therapeutic measures. Our experience suggests that early initiation of dialysis, high dialysis dose, increased frequency, and HD preferably to less efficient modalities increases survival in these 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)0.000
metaresearch head score (Gemma)0.000
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 score0.093
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0060.001

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 head0.017
GPT teacher head0.311
Teacher spread0.294 · 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

Citations31
Published2013
Admission routes1
Has abstractyes

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