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Record W2294620461 · doi:10.3109/14767058.2016.1139567

Use of peripherally inserted central catheters (PICC) via scalp veins in neonates

2016· article· en· W2294620461 on OpenAlexaff
Allison Callejas, Horacio Osiovich, Joseph Ting

Bibliographic record

VenueThe Journal of Maternal-Fetal & Neonatal Medicine · 2016
Typearticle
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsScalpMedicinePeripherally inserted central catheterSurgeryComplicationVeinUpper limbCatheterIncidence (geometry)

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this study is to describe the use and complications of peripherally inserted central catheters (PICC) via scalp veins in neonates. METHODS: A retrospective review of neonates who had PICCs inserted, between January 2010 and June 2013, in the NICU at Children's and Women's Health Center of British Columbia. RESULTS: During the study period, 689 PICCs were inserted over a total of 46 728 NICU patient days. The PICC insertion sites were scalp veins (69), upper limb veins (471), and lower limb veins (149). The mean catheter durations were 17 d, 19 d, and 18 d for PICCs inserted through scalp, upper limb, and lower limb veins, respectively. The complication rates were 23%, 23%, and 15% for insertion via scalp, upper, and lower limb veins, respectively. Centrally placed PICCs at the time of insertion were more likely to remain in situ for longer than one week (p < 0.001). The incidence of central line-associated blood stream infection was 4.4, 6.4, and 3.4 per 1000 catheter days, respectively, for scalp, upper, and lower limb PICCs. CONCLUSIONS: Insertion of PICC via the scalp veins are feasible and not associated with higher complication rates compared with insertions via other sites.

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)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.313
Teacher spread0.277 · 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

Citations33
Published2016
Admission routes1
Has abstractyes

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