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Megestrol Acetate as a Treatment for Anorexia in Hemodialysis Patients

2014· article· en· W2055479628 on OpenAlexvenueno aff
J.L. Teruel, Milagros Fernández‐Lucas, Roberto Marcén, Antonio Giner Gomis, Sandra Elías, Viviana Raoch, C. Quereda

Bibliographic record

VenueJournal of Nutritional Therapeutics · 2014
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsMegestrol acetateAnorexiaHemodialysisMegestrolMedicineEndocrinologyInternal medicine

Abstract

fetched live from OpenAlex

Background: The aim of this study was to evaluate the effectiveness of megestrol acetate as a treatment for anorexia in hemodialysis patients. Materials and Methods: From 1st January 2008 to 31st December 2010, 29 patients in our Hemodialysis Unit were treated with megestrol acetate (initial dose: 160 mg / day) for anorexia associated with a decrease in dry body weight. Sixteen patients had a protein-energy wasting syndrome. Results: Appetite improved in 25 patients, but the initial dose of megestrol acetate had to be increased in 8 patients. At three months, there was an increase in dry body weight (63.4 vs 61.9 kg, p=0.002), serum albumin level (3.98 vs 3.77 g/dl, p<0.001), serum creatinine level (10.5 vs 9.6 mg/dl, p=0.016) and protein catabolic rate (1.21 vs 0.98 g / kg / day, p < 0.001). The response was independent of the cause of anorexia. A bioelectrical impedance analysis, carried out in 9 patients, showed that treatment with megestrol acetate increased the body cell mass and changed the distribution of body water by increasing intracellular water. The megestrol acetate treatment was well-tolerated and no patients left the study due to side effects or adverse reactions. Conclusions: Megestrol acetate improves appetite and nutritional parameters in anorexic patients treated with maintenance hemodialysis.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score0.329

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)0.0000.000

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 head0.031
GPT teacher head0.308
Teacher spread0.276 · 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
Published2014
Admission routes1
Has abstractyes

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