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A third course of anti‐thymocyte globulin in aplastic anaemia is only beneficial in previous responders

2005· article· en· W1976995655 on OpenAlexaff
Vikas Gupta, E. C. Gordon‐Smith, Gordon Cook, Anne Parker, Jennifer K.M. Duguid, Keith Wilson, Qilong Yi, Judith Marsh

Bibliographic record

VenueBritish Journal of Haematology · 2005
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsAnti-thymocyte globulinMedicineRefractory (planetary science)Internal medicineGastroenterologyIncidence (geometry)Retrospective cohort studyAplastic anemiaGlobulinToxicityLimitingSurgeryBone marrow

Abstract

fetched live from OpenAlex

This retrospective study evaluated the outcome of 18 patients with aplastic anaemia treated with a third course of anti-thymocyte globulin (ATG)-containing immunosuppressive therapy (IST). Of the 18 patients, seven had responded to one of the previous courses of ATG and 11 were refractory to both the previous courses. Self-limiting grade >/=3 liver toxicity was observed in three patients. Two patients had to discontinue ATG because of severe systemic side effects. The incidence and manifestations of serum sickness did not appear to be different during the three courses. All of the seven patients that previously responded to one of the courses responded to a third course. In contrast, of 11 patients refractory to the previous courses, only two had a transient partial response. The 3-yr event-free survival for the patients who had responded to one of the previous courses of ATG was significantly superior to that of patients refractory to both the previous courses of ATG (83% vs. 0%, P = 0.0001). For aplastic anaemia patients, a third course of ATG-containing IST is a reasonable option in previous responders. Patients refractory to previous two courses of ATG have a much lower response rate and may be suitable candidates for novel therapeutic options.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score0.684

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 head0.013
GPT teacher head0.282
Teacher spread0.270 · 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

Citations42
Published2005
Admission routes1
Has abstractyes

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