MétaCan
Menu
Back to cohort
Record W3139454160 · doi:10.1177/23993693211002216

Cancer therapy-induced hyponatremia: A case-illustrated review

2021· review· en· W3139454160 on OpenAlexaff
Karyne Pelletier, Marko Škrtić, Abhijat Kitchlu

Bibliographic record

VenueJournal of Onco-Nephrology · 2021
Typereview
Languageen
FieldMedicine
TopicElectrolyte and hormonal disorders
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsHyponatremiaMedicineCancerAdverse effectIntensive care medicineVincaTyrosine-kinase inhibitorCancer therapyOncologyPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

Hyponatremia is the most common electrolyte disorder in patients with cancer and is associated with significant morbidity and mortality. Innovation in cancer therapies has led to substantial improvement in cancer outcomes, but also to new therapy-related toxicities, including electrolyte disturbance. Improvement in clinicians understanding of hyponatremia may mitigate adverse outcomes and improve quality of life in cancer patients. In this case-illustrated review, we discuss the mechanisms underlying drug-induced hyponatremia both in “classical” antineoplastic drugs and novel cancer therapies. Via these clinical cases, we describe hyponatremia caused by conventional chemotherapies (e.g. platinum compounds, vinca alkaloid, and alkylating agents) as well as hyponatremia related to tyrosine kinase inhibitors and other targeted therapies. We also focus on checkpoint inhibitors-induced hyponatremia, as these agents are increasingly used for a wide variety of malignancies. Lastly, we summarize therapy-related hyponatremia among recipients of newer treatments for multiple myeloma.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.901
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
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 head0.104
GPT teacher head0.420
Teacher spread0.316 · 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 designOther design
Domainnot available
GenreReview

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

Citations16
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Onco-NephrologySame topicElectrolyte and hormonal disordersFrench-language works237,207