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Record W2791595283 · doi:10.25301/jpda.264.158

Oral Manifestations and Dental Management of Patients with Chronic Renal Failure on Hemodialysis

2018· article· en· W2791595283 on OpenAlexaff
Nazish Khan, Shairose Aziz, Maria Kwiatowski, Farrukh Mirza, Reza Haider, Bilal Jamil

Bibliographic record

VenueJournal of The Pakistan Dental Association · 2018
Typearticle
Languageen
FieldHealth Professions
TopicOral and gingival health research
Canadian institutionsCanada Energy Regulator
Fundersnot available
KeywordsHemodialysisChronic renal failureMedicineIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

AIM:The incidence of Chronic renal failure (CRF) is increasing all over the world. Most of these patients will show oral signs or symptoms either related to the renal disease or its treatment. The treatment may require dietary and fluid restrictions but despite this most of the patients progress to end stage renal failure (ESRF) and require further treatment of haemodialysis, peritoneal dialysis or renal transplantation. This research aims to present the dental manifestations a patient undergoing hemodialysis may present with and also make recommendations for the general dentist to improve their patient's oral hygiene status. METHODOLOGY:Our inclusion criteria included patients suffering from chronic renal failure of either gender with age 18 years and above, non smokers and requiring hemodialysis for at least every month. Whereas,patients with unstable medical conditions requiring hospitalization other than dialysis in the past 30 days, or with co morbidities such as stroke. RESULTS:A comprehensive outline of the different manifestations in the patients undergoing hemodialysis; caries, periodontal disease, halitosis, lichen planus like lesions and xerostomia is discussed in this article along with the number of patients who seroconverted to Hepatitis B and C during the course of their treatment. CONCLUSION: Patients with Chronic renal failure presenting with significant medical histories and associated drug regimens, which may considerably affect the oral environment.

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 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 score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.021
GPT teacher head0.393
Teacher spread0.371 · 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.

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
Published2018
Admission routes1
Has abstractyes

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