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Record W2017722755 · doi:10.1111/petr.12060

Severe lung injury and lung biopsy in children post‐hematopoietic stem cell transplantation: <scp>T</scp>he differences between allogeneic and autologous transplantation

2013· article· en· W2017722755 on OpenAlexaff
Adam Gassas, Hayley Craig‐Barnes, Sharon Dell, Peter N. Cox, Tal Schechter, John Doyle, Lillian Sung, Maarten Egeler, Nades Palaniyar

Bibliographic record

VenuePediatric Transplantation · 2013
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineBiopsyLungHematopoietic stem cell transplantationSurgeryTransplantationLung biopsyInternal medicineGastroenterology

Abstract

fetched live from OpenAlex

To review outcome of children post-allogeneic (allo) and autologous (auto) SCT with severe lung injury who had lung biopsy and to determine whether the diagnoses provided by lung biopsy had an impact on outcome. Retrospective study was carried out from January 2000 to June 2010. Nine hundred and eighteen children (0-18 yr) received SCT (allo 476, auto 442), and 59 biopsies were performed in 48 patients. Most common result of lung biopsy was non-infectious inflammation and recurrent disease in allo- and autorecipients, respectively. In a multivariate analysis, survival of allorecipients who had management change was inferior (p = 0.002; HR: 3.12). These patients were extremely sick, and management change was the last attempt to stabilize their respiratory status. There was a trend toward superior survival for children who had biopsy after 100 days following SCT (p = 0.09; HR: 0.55) and a trend toward inferior survival for those with proven infections within two wk of biopsy (p = 0.07; HR: 2.14). Only 31% of allorecipients and 25% of autorecipients survived. There were no biopsy-related complications. Lung biopsy itself appears to be well tolerated, although requiring a biopsy seems to carry a poor prognosis; this seems to be due to different causes, auto (relapse), allo (non-infectious inflammation).

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.008
GPT teacher head0.226
Teacher spread0.218 · 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

Citations10
Published2013
Admission routes1
Has abstractyes

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