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Record W2947536621 · doi:10.1111/nep.13621

De‐indexed estimated glomerular filtration rates: A simple step towards improving accuracy of drug dosing of renally excreted medications in moderate to severe obesity

2019· article· en· W2947536621 on OpenAlex
Caitlyn Vlasschaert, Stéphane Thibodeau, Malvinder S. Parmar

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

VenueNephrology · 2019
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsNOSM University
Fundersnot available
KeywordsMedicineRenal functionDosingKidney diseaseDiscontinuationBody surface areaUrologyBody mass indexInternal medicineObesityKidneyDiabetes mellitusDrugEndocrinologyPharmacology

Abstract

fetched live from OpenAlex

ABSTRACT Kidney function is underestimated in obese individuals when standard equations are applied. Laboratory‐reported estimated glomerular filtration rates (eGFR) report glomerular filtration rates corrected for body surface area in mL/min per 1.73 m 2 using modification of diet in renal disease or the chronic kidney disease‐Epidemiology Collaboration equations. This may result in premature discontinuation or reduction in dosage of renally excreted medications. Currently, there are no clinical guidelines defining thresholds beyond which physicians should consider de‐indexing patient eGFR values. We compared standard and de‐indexed eGFR values for 281 consecutive patients seen in our chronic kidney disease clinic. In our study, half of the patients with a body mass index above 35 had clinically significant changes in their eGFR, with an improvement in chronic kidney disease stage, when eGFR was de‐indexed. We propose that eGFR de‐indexing should be considered in patients with moderate to severe obesity when calculating the dose, especially for medications that are excreted by the kidneys.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.709
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.012
GPT teacher head0.293
Teacher spread0.280 · 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