MétaCan
Menu
← Back to cohort

Management of the Surgical Patient with End‐Stage Renal Disease

2003· article· en· W2074502181 on OpenAlexvenueno aff
Amy L. Friedman

Bibliographic record

VenueHemodialysis International · 2003
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHemodialysisDialysisPeritoneal dialysisIntensive care medicineEnd stage renal diseaseInternal jugular veinSurgeryNephrology

Abstract

fetched live from OpenAlex

For health providers guiding the end-stage renal disease patient's care, conventional surgical and anesthetic principles may demand substantial modifications to optimize both the immediate outcome and long-term survival. Elective operation is conducted in the setting of optimized acid/base balance, volume status and potassium management with pre-operative dialysis completed within 24 hours of the procedure (either hemodialysis or peritoneal dialysis). When peripheral intravenous access is inadequate, central venous access is obtained preferentially through the internal jugular veins because of the catastrophic consequences of catheter-induced subclavian stenosis. Infectious prophylaxis incorporates antibiotic selection considering the lack of native renal drug clearance, the potential for drug loss across dialysis membranes and potential interactions with immunosuppressive medications. Judicious intraoperative blood and fluid replacement should not be so restricted as to permit inadequate end organ perfusion, even if post-operative dialysis is necessitated. Hemostasis may be facilitated by the use of desmopressin to correct uremic platelet dysfunction. Wound closure techniques should accommodate the delayed healing associated with kidney failure and/or iatrogenic immunosuppression.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.009
GPT teacher head0.242
Teacher spread0.233 · 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 designNot applicable
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

Citations10
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueHemodialysis International→Same topicDialysis and Renal Disease Management→French-language works237,207→