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Record W104527461 · doi:10.1155/2012/507174

The ‘Natural History’ of Declined Outpatient Gastroenterology Referrals

2012· article· en· W104527461 on OpenAlexaffvenueabout
Emelie M de Boer, David Pincock, Sander Veldhuyzen van Zanten

Bibliographic record

VenueCanadian Journal of Gastroenterology · 2012
Typearticle
Languageen
FieldMedicine
TopicAppendicitis Diagnosis and Management
Canadian institutionsUniversity of Alberta HospitalAlberta Hospital Edmonton
Fundersnot available
KeywordsMedicineNatural historyFecal occult bloodOccultAbdominal painInternal medicineGeneral surgeryMedical diagnosisGastroenterologyColonoscopyPathologyColorectal cancerAlternative medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the 'natural history' of outpatients who were referred to the Division of Gastroenterology at the University of Alberta Hospital (Edmonton, Alberta) for gastrointestinal problems and were subsequently declined. METHODS: Patients were tracked for 12 months after they were referred and declined for the following indications: abdominal pain, rectal bleeding, fecal occult blood test-positive stools and iron deficiency. For each patient, data regarding consultations by other gastroenterologists or surgeons working in the region, clinically relevant diagnoses and the number of gastrointestinal-related x-rays performed were obtained. RESULTS: Of a total sample size of 230 patients, 110 (47.8%) were seen by another gastroenterologist or surgeon after decline. A significant diagnosis was made in 21 patients (9.1%), which had immediate clinical consequences in 29%. Forty per cent of patients underwent one or more gastointestinal-related x-rays before being declined, which increased to 55% after decline. CONCLUSION: Approximately 50% of declined patients were seen by other gastroenterologists or surgeons in the region. In 9.1% of these patients, a clinically important diagnosis was made, of which one-quarter had immediate medical consequences.

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)0.000
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 score0.217
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.027
GPT teacher head0.255
Teacher spread0.228 · 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

Citations4
Published2012
Admission routes3
Has abstractyes

Explore more

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