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Record W3019350355 · doi:10.14740/jh626

Management of Iron-Deficiency Anemia on Inpatients and Appropriate Discharge and Follow-Up

2020· article· en· W3019350355 on OpenAlexvenueno aff
Kishan Patel, Zain Iqbal Memon, Rebecca Mazurkiewicz

Bibliographic record

VenueJournal of Hematology · 2020
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEsophagogastroduodenoscopyAnemiaIron deficiencyIron-deficiency anemiaColonoscopyBlood transfusionPopulationPediatricsSurgeryInternal medicineCancerColorectal cancerEndoscopy

Abstract

fetched live from OpenAlex

BACKGROUND: The aims of the study were to identify appropriate supplementation of iron for inpatients and to identify factors involved in appropriate discharge documentation and follow-up. METHODS: This was a retrospective analysis of 103 patients at a community hospital in New York City. RESULTS: A total of 57 (57/103, 55.3%) patients were admitted due to symptomatic anemia. Twenty (20/103, 19.4%) of those with iron-deficiency anemia had either esophagogastroduodenoscopy or colonoscopy. Gastroenterologist or hematologist was consulted for 45/103 (43.7%). Inpatient iron supplementation was given for 62/103 (60.2%) of patients; and 43/103 (41.7%) had blood transfusion. Upon discharge, 50/103 (48.5%) had appropriate documentation of iron-deficiency anemia on discharge paperwork. Appropriate follow-up was done for 54/103 (52.4%). Iron supplementation was provided for 53/103 (51.5%) of patients. Having inpatient esophagogastroduodenoscopy or colonoscopy, blood transfusion, or symptomatic anemia had a statistical significance for likelihood of appropriate discharge documentation. CONCLUSIONS: Iron-deficiency anemia can have high rates of mortality and morbidity in the population. Appropriate discharge of patients with iron-deficiency anemia and factors related to this are paramount for clinicians in order to have the best patient outcomes.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.239

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 head0.013
GPT teacher head0.256
Teacher spread0.243 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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