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Record W1981387406 · doi:10.1002/eat.20135

Bacterial infections in anorexia nervosa: Delayed recognition increases complications

2005· article· en· W1981387406 on OpenAlexaff
Rhonda Brown, Roger Bartrop, P. J. V. Beumont, C. Laird Birmingham

Bibliographic record

VenueInternational Journal of Eating Disorders · 2005
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComplicationMedicineAnorexia nervosaAnorexiaInternal medicineImmunologySurgeryEating disordersPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: We compared the natural history of bacterial infection in patients with anorexia nervosa (AN) with controls, and assessed which of a range of patient characteristics were associated with infection, fever response, and the rate of infectious complications in AN patients. METHOD: The charts of 311 consecutive hospital admissions of AN patients were reviewed. Patients who had a bacterial infection while in the hospital were compared with the AN patients who did not have an infection, with respect to a range of demographic and disease variables. Fever response and infection complication rate also were evaluated in AN patients with a bacterial infection and in nonanorectic control subjects admitted with a bacterial infection. RESULTS: AN patients with a bacterial infection showed a reduced fever response, were often difficult to diagnose because of fewer signs and symptoms, and infection became more frequent with increasing patient age. DISCUSSION: A reduction in fever response and the signs and symptoms of infection significantly delayed diagnosis in AN patients and increased the complication rate from bacterial infection. We recommend that an increased index of suspicion and an early complete blood count and bacteriologic cultures be adopted for the investigation of bacterial infection in AN patients.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.333
Teacher spread0.309 · 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.

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

Citations59
Published2005
Admission routes1
Has abstractyes

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