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Record W2211624312 · doi:10.1155/2016/9431385

Predictors of Poor Outcomes in Critically Ill Adults with Hematologic Malignancy

2016· article· en· W2211624312 on OpenAlexafffundabout
Marion Cornish, Michael Butler, Robert S. Green

Bibliographic record

VenueCanadian Respiratory Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsDalhousie University
FundersFaculty of Medicine, Dalhousie UniversityDalhousie University
KeywordsMedicineIntensive care unitMechanical ventilationHematologic malignancyLogistic regressionMultiple myelomaEmergency medicineInternal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

Background. Patients with hematologic malignancy (HM) often require intensive care unit (ICU) admission due to organ failure through disease progression or treatment-related complications. Objective. To determine mortality and prognostic variables in adult patients with HM who were admitted to ICU. Methods. Structured chart review of all adult patients (age ≥ 18 years) with HM admitted to ICU of a Canadian tertiary care hospital between 2004 and 2014. Outcome measures included mortality (ICU, 30-day, 60-day, and 12-month). Logistic regression was performed to determine predictors of mortality. Results. Overall, there were 206 cases of HM admitted to the ICU during the study (mean age: 51.3 ± 13.6 years; 60% male). Median stay was 3 days, with 14.1% requiring prolonged ICU admission. ICU mortality was 45.6% and increased to 59.2% at 30 days, 62.6% at 60 days, and 74.3% at 12 months. Predictors of increased ICU mortality included mechanical ventilation requirement and vasopressor therapy requirement, while admission to ICU postoperatively and having myeloma were associated with decreased mortality. Conclusions. Patients admitted to ICU with HM have high mortality (45.6%), which increased to 74.3% at 1 year. Analysis of multiple variables identified critical illness, postsurgical admission, and myeloma as predictors of patient outcomes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.015
GPT teacher head0.258
Teacher spread0.243 · 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

Citations24
Published2016
Admission routes3
Has abstractyes

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