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Thrombotic thrombocytopenic purpura/haemolytic uraemic syndrome: a new index predicting response to plasma exchange

2005· article· en· W2061175590 on OpenAlexafffundabout
B. F. Wyllie, Amit X. Garg, Jennifer J. Macnab, G. Rock, William F. Clark

Bibliographic record

VenueBritish Journal of Haematology · 2005
Typearticle
Languageen
FieldImmunology and Microbiology
TopicComplement system in diseases
Canadian institutionsOttawa HospitalUniversity of OttawaWestern University
FundersCanadian Institutes of Health Research
KeywordsThrombotic thrombocytopenic purpuraMedicineLogistic regressionInternal medicineCreatininePopulationGastroenterologyRetrospective cohort studyPlateletPediatrics

Abstract

fetched live from OpenAlex

Despite the favourable response of thrombotic thrombocytopenic purpura/haemolytic uraemic syndrome (TTP/HUS) to plasma exchange, an early level of mortality persists. Non-response has been associated with a low frequency of exchange. The Rose index of TTP/HUS severity, occasionally used to predict the response of TTP/HUS to plasma exchange, remains unsatisfactory. The purpose of this study was to develop a new index predicting response of TTP/HUS to plasma exchange and to compare it with the Rose index. Retrospective analysis of 171 cases of TTP/HUS from 39 apheresis units across Canada between 1980 and 2001 was conducted. Logistic regression analysis was used to derive a model predicting 6-month mortality from presenting characteristics. The reduced model contained age >40 years, haemoglobin <9.0 g/dl and the presence of a fever at presentation. Gender, platelet count, creatinine and neurological signs were not part of the final model. This model predicted 13.4% of outcome variance. Predictive scores of 0, 2, 4 and 6 correlated with 6-month mortality rates of 12.5%, 14.0%, 31.3% and 61.5% respectively in our source population. This simple model may help identify those patients who would benefit from higher treatment intensity.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.017
GPT teacher head0.264
Teacher spread0.247 · 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; both teacher heads agree on what is shown here.

Study designCase report
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

Citations66
Published2005
Admission routes3
Has abstractyes

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