MétaCan
Menu
← Back to cohort
Record W4394284588 · doi:10.6084/m9.figshare.8319695

Supplementary Material for: GFR-Specific versus GFR-Agnostic Cutoffs for Parathyroid Hormone and Fibroblast Growth Factor-23 in Advanced Chronic Kidney Disease

2019· dataset· en· W4394284588 on OpenAlexaboutno aff
Mark Canney, Ognjenka Djurdjev, Mila Tang, Claudia Zierold, Frank Blocki, Myles Wolf, Adeera Levin

Bibliographic record

VenueFigshare · 2019
Typedataset
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsFibroblast growth factor 23Parathyroid hormoneInternal medicineEndocrinologyMedicineRenal functionKidney diseaseUrologyCalcium

Abstract

fetched live from OpenAlex

Background: In the majority of patients with advanced chronic kidney disease (CKD), values of parathyroid hormone (PTH1–84) and fibroblast growth factor 23 (FGF-23) exceed the normal reference range, potentially as an appropriate adaptation to reduced glomerular filtration rate (GFR). We tested whether GFR-specific cutoffs for PTH1–84 and FGF-23 could better identify patients with inappropriately high PTH1–84 and FGF-23 for their degree of CKD and thereby improve prognostication of clinical outcomes compared to a uniform threshold. Methods: Prospective pan-Canadian cohort of 1,812 patients with mean estimated GFR (eGFR) 28.9 mL/min/1.73 m2 followed for a median of 52 months. Repeated log-rank tests were used to identify optimal cutoffs for PTH1–84 and FGF-23 within eGFR strata (<20, 20–29 and ≥30 mL/min/1.73 m2) that maximally differentiated high- and low-risk populations for (1) cardiovascular (CV) events (fatal or nonfatal myocardial infarction, coronary revascularization, stroke, heart failure) and (2) renal events (initiation of chronic renal replacement therapy). In multivariable models, we examined the association between ­GFR-specific cutoffs and outcomes and compared their added prognostic value to existing uniform thresholds. Results: Risk-based cutoffs for PTH1–84 and FGF-23 increased in a graded fashion with decreasing eGFR. Among patients with eGFR <20 mL/min/1.73 m2, CV risk-based cutoffs for PTH1–84 and FGF-23 were 3.4 and 5.5 times the upper limit of normal, respectively, and reclassified 31.9 and 35.1% of patients when added to a multivariable base model for CV events. In contrast, the addition of PTH1–84 and FGF-23 to the base model using uniform cutoffs failed to reclassify such patients. Similar findings were demonstrated for renal outcomes. Conclusion: GFR-specific risk-based cutoffs for PTH1–84 and FGF-23 may facilitate more meaningful risk stratification in advanced CKD than current GFR-agnostic reference ranges derived from healthy adults. This may be most applicable in those with severely reduced GFR.

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)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.757
Threshold uncertainty score0.346

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.7570.169

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 head0.034
GPT teacher head0.300
Teacher spread0.266 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueFigshare→Same topicParathyroid Disorders and Treatments→French-language works237,207→