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Record W2115843715 · doi:10.1177/089686080202200207

Normal or Low Initial PTH Levels are not a Predictor of Morbidity/Mortality in Patients Undergoing Chronic Peritoneal Dialysis

2002· article· en· W2115843715 on OpenAlexaff
Nada Dimković, Joanne M. Bargman, Stephen I. Vas, Dimitrios G. Oreopoulos

Bibliographic record

VenuePeritoneal Dialysis International · 2002
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsToronto Western HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicinePeritoneal dialysisIncidence (geometry)Univariate analysisDialysisInternal medicineLogistic regressionParathyroid hormoneAlfacalcidolKidney diseaseSurgeryMultivariate analysisUrologyOsteoporosisBone mineralCalcium

Abstract

fetched live from OpenAlex

OBJECTIVE: During the past few decades, the pattern of bone disease in uremic patients has changed significantly. There has been an increase in the number of patients with normal or low initial parathyroid hormone (PTH) levels, particularly in patients on chronic peritoneal dialysis (CPD). Previous authors have described a higher prevalence of bone pain, microfractures, and fractures, and higher mortality among these patients. The aim of this study was to determine the incidence, morbidity, and mortality of patients who had a low or normal intact PTH (iPTH) level when they started CPD. DESIGN: We reviewed the records of 251 patients in our program that started CPD during the past 5 years (January 1996-December 2000). Clinical data, laboratory variables, medication, and dialysis parameters/dose were available at every clinic visit (approximately every 4 weeks). Intact PTH was used to express parathyroid function; values 3 times higher than the upper limit of normal (ULN) were assumed to be optimal. Variables predictive of the development of parathyroid dysfunction were calculated by univariate and multivariate logistic regression analysis. RESULTS: Of the patients who started CPD, 15.5% had iPTH values below the ULN (7.6 pmol/L), and an additional 29.5% had an iPTH of less than 3 times the ULN (i.e., between 7.6 and 22.8 pmol/L). We call these two groups of patients the normal/low initial iPTH group. During the follow-up period (3-63 months), we found a trend toward increasing iPTH levels. By the end of the study period, 61.2% of those with normal/low initial iPTH remained in the normal/low iPTH range, and 38.8% had converted to a group with an iPTH range higher than 22.8 pmol/L. The patients who converted their iPTH grouping were younger, fewer of them were diabetics (p = not significant), and they were more frequently on low calcium dialysate (p < 0.05). Hyperphosphatemia was an independent risk factor for subsequent iPTH changes during the course of continuous ambulatory PD treatment. All patients in the normal/low iPTH groups had a low prevalence of bone fractures (3.5%). Also, patients who remained in the normal/low iPTH group at the end of the follow-up period did not have more fractures than those who converted to the hyperparathyroid group (3.8% vs 3.1%). We found no differences in bone fractures between patients with iPTH levels below 22.8 and those with levels above 22.8 pmol/L (3.5% vs 5.4%), nor were there differences in patient and technique survival between these two groups. CONCLUSION: Normal/low initial iPTH is a frequent finding among patients starting CPD. Serum phosphorus was an independent risk factor for subsequent iPTH changes during the course of CPD treatment. Use of low calcium dialysate was significantly higher in patients who converted their iPTH into the high iPTH range. Very few patients with low/normal iPTH had bone-related symptoms (pain and fractures), and their morbidity and mortality did not differ from those patients with a high initial iPTH level.

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)0.005
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.048
GPT teacher head0.317
Teacher spread0.269 · 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

Citations19
Published2002
Admission routes1
Has abstractyes

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