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Record W2600339273

How often are patients with diabetes or hypertension being treated with partial nephrectomy for renal cell carcinoma? A population-based analysis

2011· article· en· W2600339273 on OpenAlexaffabout
Robert Abouassaly, Antonio Finelli, George Tomlinson, David R. Urbach, Shabbir M.H. Alibhai

Bibliographic record

VenueJournal of Endourology · 2011
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineNephrectomyRenal cell carcinomaKidney diseaseDiabetes mellitusRenal functionPopulationDiseaseCohortInternal medicineNephrologyIntensive care medicineSurgeryKidneyEndocrinology
DOInot available

Abstract

fetched live from OpenAlex

Study Type – Therapy (cohort) Level of Evidence 2b What’s known on the subject? and What does the study add? We know that a major benefit of partial nephrectomy (PN) over radical nephrectomy (RN) is greater preservation of kidney function. Emerging evidence also suggests that chronic kidney disease (CKD) correlates with survival, likely as a result of increased cardiovascular morbidity. We also know that Diabetes Mellitus (DM) followed by Hypertension (HTN) are the two most frequent causes of end-stage renal disease (ESRD). Given the strong association between renal functional decline and the surgical treatment of small renal masses, one would expect utilization of PN in patients with HTN or DM to be high, however minimal data exist on PN use in these populations. We are thus unable to determine whether these patients are being managed optimally. Our large study demonstrates that PN is being underutilized in patients at risk for CKD, particularly patients with Diabetes Mellitus and Hypertension. Unlike previously published reports, our population-level study provides a description of the landscape of care for patients with renal masses in general practice, and does not simply reflect treatment patterns at tertiary referral centers. The finding of low PN use in patients at risk for CKD deserves further study. Future studies should focus on determining the specific factors contributing to PN underutilization in these susceptible patients, as well as developing clinical tools to reliably identify those patients in whom the benefits of PN outweigh the risks. OBJECTIVE •  To determine partial nephrectomy (PN) use in patients at risk of chronic kidney disease (CKD), such as those with diabetes mellitus (DM) and hypertension (HTN). PATIENTS AND METHODS •  We conducted a national, population-based, retrospective, observational study using the Canadian Institute for Health Information Discharge Abstract Database. •  We included all patients treated surgically for renal cell carcinoma from 1 April 1998 to 31 March 2008. •  Patients with DM and HTN were identified using specific diagnosis codes. •  The proportions of patients treated with PN were compared in patients with and without DM and HTN using multivariable logistic regression adjusting for covariates. RESULTS •  A total of 24 579 patients were treated for a renal mass; of these, 4292 (17.5%) underwent PN. •  In our sample, 5613 (22.8%) patients were identified as having HTN, and 2738 (11.1%) were identified as having DM. •  PN was used in 17.3% of patients with HTN compared to 17.5% of those without HTN, whereas, in patients with DM, PN was used in 18.6% compared to 17.3% of patients without DM. •  After adjusting for covariates, neither HTN, nor DM were found to be independently associated with increased PN use (odds ratio, 1.07; 95% CI, 0.98–1.16 and odds ratio, 1.08; 95% CI, 0.96–1.20, respectively). CONCLUSIONS •  In this contemporary national analysis, PN appears to be underutilized in DM and HTN, despite their known relationship with chronic renal failure. •  Further studies are needed to elucidate the specific factors contributing to PN underutilization in these susceptible patients.

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)0.000
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 score0.034
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.024
GPT teacher head0.211
Teacher spread0.186 · 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".

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Citations0
Published2011
Admission routes2
Has abstractyes

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