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Record W2118877880 · doi:10.12968/gasn.2007.5.7.27194

Physical assessment in gastroenterology: abdominal examination

2007· article· en· W2118877880 on OpenAlexaff
Fiona Hibberts, Christine Bushell

Bibliographic record

VenueGastrointestinal Nursing · 2007
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicineCompetence (human resources)OfficerNursingMedical educationManagement

Abstract

fetched live from OpenAlex

The role of the specialist nurse continues to develop and it is therefore vital that new knowledge and skills accompany the transition. Increasingly, specialist nurses need to swiftly and accurately assess, examine, diagnose and treat patients autonomously (Royal College of Nursing (RCN), 2002). This expansion of skills and knowledge is also reflected in the Chief Nursing Officer′s ten keys roles for nurses (Department of Health (DH), 2002). Academic and theoretical study should be combined with practical and supervised case studies in order to gain competence in this skill.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0150.009

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 head0.037
GPT teacher head0.444
Teacher spread0.407 · 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 designNot applicable
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

Citations4
Published2007
Admission routes1
Has abstractyes

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