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Record W2521333572 · doi:10.1080/10428194.2016.1213825

Chest CT scans are frequently abnormal in asymptomatic patients with newly diagnosed acute myeloid leukemia

2016· article· en· W2521333572 on OpenAlexafffund
Janaki Vallipuram, Sidika Dhalla, Chaim M. Bell, Linda Dresser, Heekyung Han, Shahid Husain, Mark D. Minden, Narinder Paul, Miranda So, Marilyn Steinberg, Mayuran Vallipuram, Gary Wong, Andrew M. Morris

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2016
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsPrincess Margaret Cancer CentreMount Sinai HospitalInstitute for Clinical Evaluative SciencesUniversity Health NetworkUniversity of Toronto
FundersUniversity Health Network
KeywordsAsymptomaticMedicineRadiologyMyeloid leukemiaPneumoniaFebrile neutropeniaNeutropeniaInternal medicine

Abstract

fetched live from OpenAlex

Chest computed tomography (CT) findings of nodules, ground glass opacities, and consolidations are often interpreted as representing invasive fungal infection in individuals with febrile neutropenia. We assessed whether these CT findings were present in asymptomatic individuals with acute myeloid leukemia (AML) at low risk of invasive fungal disease. A retrospective study of consecutive asymptomatic adult patients with newly diagnosed AML over a 2-year period was performed at a tertiary care oncology center. Radiology reports of baseline chest CTs were reviewed. Of 145 CT scans, the majority (88%) had pulmonary abnormalities. Many (70%) had one or both of unspecified opacities (52%) and nodules (49%). Ground glass opacities (18%) and consolidations (12%) occurred less frequently. Radiologists suggested pneumonia as a possible diagnosis in 32% (n = 47) of scans. Chest CT may result in over-diagnosis of invasive fungal disease in individuals with febrile neutropenia if interpreted without correlation to the patients' clinical status.

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 score0.144
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 studies0.0000.001
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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

Citations6
Published2016
Admission routes2
Has abstractyes

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