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Overview of the use of implantable venous access devices in the management of children with inherited bleeding disorders

2006· review· en· W2066394125 on OpenAlexaff
Patcharee Komvilaisak, Bairbre Connolly, Ali Raza Naqvi, Victor S. Blanchette

Bibliographic record

VenueHaemophilia · 2006
Typereview
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineVenous accessMEDLINEIntensive care medicinePediatricsSurgery

Abstract

fetched live from OpenAlex

Frequent infusion of factor concentrates may be challenging in young boys with haemophilia, especially if their disease is complicated by inhibitors. A central venous access device (CVAD) is often placed in young patients in need of repeated infusions for prophylaxis or immune tolerance induction. Although user friendly and capable of providing reliable venous access, these devices are associated with a high complication rate over time. In the haemophilia population, major complications include CVAD-associated infections and deep venous thrombosis, which is most often silent. Established risk factors for catheter-related infection include age less than 6 years at the time of CVAD placement and use of an external CVAD when compared with a totally implantable device such as a port. Avoidance of CVAD-related infections is facilitated by strict adherence to aseptic technique. The risk of deep venous thrombosis appears related to the duration for which the catheter is in place, with the risk increasing beyond 4 years. The promotion of a strict clinic policy in which CVADs are left in place for as short a time as possible should decrease the risk of complications. In rare cases where a totally implantable CVAD cannot be placed for technical reasons, an arteriovenous fistula may provide reliable venous access. In all cases, however, venous access via peripheral veins is preferred over CVADs.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.766
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.179
GPT teacher head0.393
Teacher spread0.214 · 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 designOther design
Domainnot available
GenreReview

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

Citations22
Published2006
Admission routes1
Has abstractyes

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