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Record W4381158321 · doi:10.1080/21548331.2023.2225964

Analysis of the economic burden of docusate sodium at a United States tertiary care center

2023· article· en· W4381158321 on OpenAlexaboutno aff
Alexander J. Kaye, Suzanne H. Atkin, Aidan Ziobro, Jason Donnelly, Sushil Ahlawat

Bibliographic record

VenueHospital Practice · 2023
Typearticle
Languageen
FieldMedicine
TopicEsophageal and GI Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTertiary careSalaryMedical prescriptionPopulationHealth careEmergency medicineFamily medicineDemographyPediatricsEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVES: The primary objective was to determine the financial resources allocated to docusate at a representative U.S. tertiary care center. Secondary objectives included comparing docusate utilization between two tertiary care centers, and exploring alternative uses for the funds spent on docusate. METHODS: , 2019 was collected. The annual total cost associated with docusate use per year was calculated. The 2015 data from this study and a 2015 McGill University Health Centre study were compared. Also, alternative uses for the money utilized on docusate were assessed. RESULTS: Over the study period, 37,034 docusate prescriptions and 265,123 docusate doses were recorded. The average cost of prescribing docusate was $25,624.14 per year and $49.37 per hospital bed per year. A comparison between the 2015 data of University Hospital and McGill showed that McGill prescribed 107 doses and spent $10.09 more per hospital bed than University Hospital. Finally, alternative uses for the average yearly spending on docusate equated to 0.35 the salary of a nurse, 0.51 the salary of a secretary, 20.66 colonoscopies, 27.00 upper endoscopies, 186.71 mammograms, 1,399.37 doses of polyethylene glycol 3350, 3,826.57 doses of lactulose, or 4,583.80 doses of psyllium. CONCLUSION: A single average size tertiary care hospital spent about $25,000 yearly on docusate despite its lack of clinical effectiveness. While this amount is small compared to an overall hospital budget, when considering likely comparable docusate use at the U.S's 6,090 hospitals, the economic burden of docusate becomes significant. The funds currently being used on docusate could be redirected to alternative, more cost-effective purposes.

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 score0.019
Threshold uncertainty score0.249

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.276
Teacher spread0.269 · 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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