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Record W2620842396 · doi:10.1159/000474948

Survey of Techniques Utilized to Access Ventricular Shunts and Reservoirs

2017· article· en· W2620842396 on OpenAlexaff
Claudia I. Martinez, Stephen A. Fletcher, Manish N. Shah, Marcia Kerr, David I. Sandberg

Bibliographic record

VenuePediatric Neurosurgery · 2017
Typearticle
Languageen
FieldNeuroscience
TopicCerebrospinal fluid and hydrocephalus
Canadian institutionsPediatric Oncology Group
Fundersnot available
KeywordsMedicineNeurosurgerySubspecialtySurgeryInternal medicineFamily medicine

Abstract

fetched live from OpenAlex

AIMS: This study assessed variations in pediatric neurosurgical technique when accessing shunts and ventricular access devices (VADs). METHODS: A 12-question survey was developed and sent to members of the American Association of Neurological Surgeons (AANS) whose self-identified subspecialty was pediatric neurosurgery. RESULTS: Four hundred and twenty surveys were sent out, and 149 responses were received (35.5% response rate); 95.3% of respondents always use sterile gloves, 55.0% never use a sterile gown, and 69.8% always have a member of the neurosurgery team perform the procedure. The majority of respondents answered "sometimes" for use of a facemask (38.3%), sterile drapes (39.6%), site shaving (45.6%), having an attending present (68.5%), and having an assistant hold the patient's head (78.5%). The majority reported using a 23- or 25-gauge butterfly needle for site entry (96.6%), and betadine or ChloraPrep™ as the preferred antiseptic solution (64.4%). The frequency in which CSF is sent for analysis is not standardized in 31.5% of respondents, and wait time for the antiseptic solution to dry is not standardized in 62.4%. CONCLUSIONS: There is great variation in the technique for accessing shunts and VADs. Future studies are needed to assess whether these discrepancies affect infection rates.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.094
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.077
GPT teacher head0.338
Teacher spread0.260 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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