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Fever of unknown origin in a hemodialysis patient with a failed allograft

2007· article· en· W2085500935 on OpenAlex
Halil Yazıcı, Mehmet Şükrü Sever

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueHemodialysis International · 2007
Typearticle
Languageen
FieldMedicine
TopicHematological disorders and diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineImmunosuppressionHemodialysisFever of unknown originEtiologyIntensive care medicineDifferential diagnosisPopulationUremiaAbdominal painSurgeryInternal medicinePathology

Abstract

fetched live from OpenAlex

Fever of unknown origin (FUO) in hemodialysis (HD) patients represents a diagnostic challenge because differential diagnosis includes diverse etiologies. Causes of FUO in the general population can be classified into 3 diagnostic categories: infections, tumors, and noninfectious inflammatory diseases. Also, chronic HD patients may have additional problems such as infections, the risk for which may be increased by the immunosuppression associated with uremia, vascular access-related infections, and nosocomial infections. Moreover, patients with chronically failed kidney transplants can have low-grade fever and abdominal pain, and if inflammation of the allograft is severe enough, it may result in a spontaneous rupture. Hence, it is important to rapidly recognize, diagnose, and manage these complications. In the present study, we report a case of FUO in an HD patient with a failed graft and discuss clinical approach and management of these 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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.279
Teacher spread0.265 · 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