MétaCan
Menu
← Back to cohort
Record W3097766015 · doi:10.1182/blood-2020-134557

Characterizing Frailty in Myeloproliferative Neoplasms: Baseline Results from the Orchid Study

2020· article· en· W3097766015 on OpenAlexaff
Nicholas L.J. Chornenki, Sarah Karampatos, Darryl P. Leong, Deborah Siegal, Christopher Hillis

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsJuravinski Cancer CentreMcMaster UniversityPopulation Health Research InstituteQueen's University
Fundersnot available
KeywordsMedicineInternal medicineStroke (engine)Weight lossDepression (economics)Physical therapyObesity

Abstract

fetched live from OpenAlex

Background: Contemporary treatment of Myeloproliferative Neoplasms (MPNs) has altered their natural history. Frailty, a state of reduced physiologic reserve is prevalent among older individuals, and increases risk for cardiovascular disease (CVD) and other adverse health outcomes. However, little is known about frailty in MPNs. An improved characterization of frailty may inform MPN treatment goals including the impact of treatment on frailty and CVD. Methods : We utilized the ORCHID database, a multi-centre prospective observational registry of MPN patients. Frailty-related factors collected included: unintentional weight loss (>4kg lost in prior year); grip strength (below 20th percentile for age and sex); Timed Up and Go test (TGUG) greater than 13.5 seconds (indicative of high fall risk); self-reported exhaustion; and cognitive frailty measured by the Digit Symbol Substitution test (DSST). Baseline information on cardiovascular event history was also collected. Results: In total, 150 patients were included (56 CML, 28 ET, 14 PMF, 52 PV). Baseline information and past medical history are detailed in Table 1. Cardiovascular events prior to diagnosis were most common in patients with PV (7.7% MI and 5.8% stroke). Past medical history of Venous Thromboembolism (VTE) was more common in Ph-negative MPNs (15.3% PV, 21.4% PMF, 21.4% ET) compared to CML where VTE history was absent. Hypertension and dyslipidaemia were overall the most common cardiovascular comorbidities. The burden of depression was highest in PMF with 30.7% of patients reporting a PHQ-9 score ≥ 10. Frailty related outcomes are detailed in Table 2. All four MPNs had high rates of frailty in every domain at baseline. Among Ph- MPNs (ET, PV, and PMF), ET and PV patients generally had a lower burden of frailty compared to PMF patients in most measurements. A high risk of falls as determined by a prolonged get-up-and-go test was present in approximately 20% of PV, CML, and ET patients and 42% of PMF patients. ET patients had the lowest self-reported exhaustion, with less than 40% reporting they ever felt "everything was an effort" or that they "just couldn't get going". PMF patients had the highest self-reported exhaustion. The proportion of patients with low DSST scores was greater in PMF (38.5%) and PV patients (24.3%) than ET (20%) and CML (15%) patients. Unintentional weight loss was common across MPN patients (33.9% to 50%). Conclusion: There is a high rate of baseline frailty among patients with MPNs with domains differing between diseases. In this cohort, cardiovascular event history at diagnosis was highest among PV patients and lowest in CML patients. Disclosures Siegal: Leo Pharma: Honoraria; Novartis: Honoraria; Portola: Honoraria; Bayer: Honoraria; BMS Pfizer: Honoraria. Hillis:Roche: Honoraria.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.053
GPT teacher head0.305
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueBlood→Same topicAcute Myeloid Leukemia Research→French-language works237,207→