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Record W2032958454 · doi:10.1258/ult.2011.010053

My patient has no blood pressure: are they empty or full? Point-of-care ultrasound of the inferior vena cava in the hypotensive emergency department patient

2011· article· en· W2032958454 on OpenAlexaff
Paul Atkinson, C. Daly

Bibliographic record

VenueUltrasound · 2011
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsSaint John Regional HospitalDalhousie University
Fundersnot available
KeywordsMedicineEmergency departmentPoint of care ultrasoundMedical diagnosisResuscitationIntensive care medicineEmergency ultrasoundRadiological weaponInferior vena cavaPatient careUltrasoundPoint of careFocused assessment with sonography for traumaMedical emergencyEmergency medicineRadiologyNursing

Abstract

fetched live from OpenAlex

Bedside, focused or point-of-care ultrasound (PoCUS) is becoming an established technique within emergency and critical care medicine to answer time-dependent, focused clinical questions. Bedside sonography is not a complete radiological investigation, rather an extension of the clinical examination to rule in or rule out key diagnoses in specific clinical settings. PoCUS is geared to addressing highly time-dependent and focused questions, and in general most focused scans become more obviously positive as the patient becomes increasingly unwell. In the hypotensive patient, one of the first questions a clinician must address is whether the patient requires emergency fluid resuscitation. That is, is the patient under-filled or overloaded? So, how can we use ultrasound to add value to our clinical assessment of filling in the hypotensive patient?

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.040
GPT teacher head0.279
Teacher spread0.239 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

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