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Record W2978042176 · doi:10.1097/md.0000000000016988

Two successful insertions of peripherally inserted central catheters in a patient with persistent left superior vena cava

2019· article· en· W2978042176 on OpenAlexaff
Junren Kang, Bing Liu, Wenyan Sun

Bibliographic record

VenueMedicine · 2019
Typearticle
Languageen
FieldMedicine
TopicVascular anomalies and interventions
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsMedicinePersistent left superior vena cavaPeripherally inserted central catheterThrombosisSurgeryVenous thrombosisCatheterCardiologyCoronary sinus

Abstract

fetched live from OpenAlex

RATIONALE: Peripherally inserted central catheters (PICC), normally located at the lower 1/3rd of the superior vena cava (SVC) and cavo-atrial junction, are commonly used in cancer patients. Persistent left superior vena cava (PLSVC) is a vascular anomaly, in patients with which seldom research was reported about PICC implanted. After obtaining written informed consent, we present a case where two successful insertions of PICC were performed in a 50-year-old female patient with PLSVC and right SVC. PATIENTS CONCERNS: The patient had ovarian cancer and was admitted for chemotherapy using PICC. DIAGNOSES: Ovarian cancer and PLSVC. INTERVENTIONS AND OUTCOMES: Following insertion of PICC in PLSVC, thrombosis developed. PICC was removed after routine anticoagulation therapy. Owing to tumor recurrence, a second PICC was inserted in the right SVC without any complications. LESSONS: PICC insertion in PLSVC for chemotherapy may be associated with an increased risk of deep venous thrombosis of the upper extremity. A right catheter insertion in patient with PLSVC was preferred.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.237
Teacher spread0.229 · 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 designCase report
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

Citations6
Published2019
Admission routes1
Has abstractyes

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