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Record W2992103971 · doi:10.1182/hematology.2019000041

Integrating patient-centered factors in the risk assessment of MDS

2019· review· en· W2992103971 on OpenAlex
Rena Buckstein

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHematology · 2019
Typereview
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsSunnybrook Health Science Centre
FundersCelgene
KeywordsMedicineRisk assessmentIntensive care medicineOncologyComputer science

Abstract

fetched live from OpenAlex

Myelodysplastic syndromes are clonal myeloid neoplasms that primarily present in older adults. Although leukemia develops in approximately 25% to 30% of individuals, the significantly shortened survival in this population is attributed more commonly to nonleukemic causes. The current prognostic scoring systems for leukemia and overall survival based on disease characteristics are becoming increasingly sophisticated and accurate with the incorporation of molecular data. The addition of patient-related factors such as comorbidity, disability, frailty, and fatigue to these new models may improve their predictive power for overall survival, treatment toxicity, and health care costs. To improve the generalizability of clinical trial results to the real world, geriatric assessment testing should become a standard of care in MDS clinical trials.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.090
GPT teacher head0.421
Teacher spread0.331 · 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