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Catheter Outcomes in Home Infusion

2008· article· en· W2045630652 on OpenAlexaff
Melissa Leone, L. Rad Dillon

Bibliographic record

VenueJournal of Infusion Nursing · 2008
Typearticle
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsDillon Consulting
Fundersnot available
KeywordsBloodstream infectionMedicineCatheterIntensive care medicineLiberian dollarHealth careEmergency medicineSurgeryBusiness

Abstract

fetched live from OpenAlex

In Brief Intravenous catheter outcomes are a prominent topic for healthcare providers whose patients receive intravenous medications. There are thousands of products being marketed today claiming to improve catheter outcomes, thus improving overall patient outcomes and reducing provider costs associated with catheter infections and replacement. Catheter-related bloodstream infections (CR-BSIs) cost hospitals between $5000 and $34,000 per infection, and 12% to 25% of bloodstream infections are attributable to patient mortality. Products that claim to prevent CR-BSIs and subsequently reduce the number of bloodstream infections are a multimillion-dollar industry. Intravenous catheter outcomes are a prominent topic for healthcare providers whose patients receive intravenous medications. There are thousands of products being marketed today claiming to improve catheter outcomes, thus improving overall patient outcomes and reducing provider costs associated with catheter infections and replacement. Catheter-related bloodstream infections (CR-BSIs) cost hospitals between $5000 and $34,000 per infection, and 12% to 25% of bloodstream infections are attributable to patient mortality. Products that claim to prevent CR-BSIs and subsequently reduce the number of bloodstream infections are a multimillion-dollar industry.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.046
GPT teacher head0.366
Teacher spread0.320 · 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 source (direct Gemma or distilled Codex), 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

Citations19
Published2008
Admission routes1
Has abstractyes

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