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Record W2064638441 · doi:10.3324/haematol.12152

Sickle cell disease-related organ damage occurs irrespective of pain rate: implications for clinical practice

2008· article· en· W2064638441 on OpenAlexaff
Eduard J. van Beers, Charlotte F.J. Van Tuijn, Melvin R. Mac Gillavry, A. van der Giessen, John‐John B. Schnog, Bart J. Biemond

Bibliographic record

VenueHaematologica · 2008
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsMedicineMicroalbuminuriaDiseaseAcute chest syndromePriapismChest painSickle cell anemiaInternal medicineSurgery

Abstract

fetched live from OpenAlex

In daily clinical practice, the frequency of painful crises (pain rate) is an important parameter of sickle cell disease severity. We assessed the prevalence of sickle cell disease-related organ damage and complications and their relation to pain rate. Organ damage and history of vaso-occlusive complications were obtained via systematic screening of consecutive patients and by chart review. In 104 adult sickle cell patients pain rate was related to a history of acute chest syndromes, avascular osteonecrosis, iron overload, priapism and cholelithiasis. However, major disease-related complications, such as microalbuminuria and pulmonary hypertension, were detected in 23% and 24% respectively of patients without painful crises in the study period underlining the importance of systematic screening for developing organ damage in sickle cell patients irrespective of pain rate.

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)0.008
metaresearch head score (Gemma)0.064
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.064
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.049
GPT teacher head0.355
Teacher spread0.306 · 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

Citations44
Published2008
Admission routes1
Has abstractyes

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