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Record W2157001590 · doi:10.1002/pbc.23416

Prevention of bacterial infection in pediatric oncology: What do we know, what can we learn?

2011· article· en· W2157001590 on OpenAlexafffund
Sarah Alexander, Michael L. Nieder, Danielle M. Zerr, Brian T. Fisher, Christopher C. Dvorak, Lillian Sung

Bibliographic record

VenuePediatric Blood & Cancer · 2011
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsSickKids FoundationHospital for Sick Children
FundersNational Cancer InstituteCanadian Institutes of Health Research
KeywordsMedicineBlood cancerPediatric oncologyIntensive care medicineSepsisPediatric cancerAntimicrobialCancerCancer therapyInternal medicine

Abstract

fetched live from OpenAlex

Bacterial sepsis continues to be a leading cause of morbidity and toxic death in children receiving intensive therapy for cancer. Empiric therapy for suspected infections and treatment of documented infections are well-established standards of care. The routine use of prophylactic strategies is much less common in pediatric oncology. This paper will review the current literature on the use and risks of antimicrobial prophylaxis as well as non-pharmacological methods for infection prevention and will address areas in need of further research.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.447
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.302
Teacher spread0.275 · 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

Citations41
Published2011
Admission routes2
Has abstractyes

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