Criteria for referring patients with renal disease for nephrology consultation: a review of the literature
Bibliographic record
Abstract
Abstract Introduction: Referrals to nephrologists comprise not only patients with chronic kidney disease but also those with other nephrological conditions. There may be confusion about when to refer a patient to a nephrologist. We conducted a literature review to identify preexisting priority-setting, triage or referral criteria developed to guide referrals from primary care to a nephrologist. Methods: Medline and Cochrane databases were searched (1997 to 2008) using search terms: referral, consultation, triage and a list of specified nephrological conditions. Abstracts were assessed by 2 reviewers using criteria to determine relevance. Citation and hand searches were done on papers selected for review; relevant Web sites were also consulted. Two reviewers read all selected papers to determine if they met the objectives. One reviewer abstracted relevant data from each retained reference and compiled the results into a report, which was reviewed by 3 practicing nephrologists. Results: There were 18 retained papers, reports or Web sites; 4 of these were professional national guidelines. All but 1 reference cited serum creatinine or estimated glomerular filtration rate as a criterion for referral. Other referral criteria were proteinuria (8 sources), blood pressure (5 sources), electrolytes (3 sources) or hematuria (3 sources). There was inconsistency in referral recommendations. Conclusions: The differing advice identified in the literature results in confusion as to when patients should be referred to a nephrologist. Nephrologists, an already strained human resource, must prioritize requests for consultation using an undefined and no doubt inconsistent metric. Standardized, diagnosis neutral criteria would benefit both primary care providers and nephrologists. Results: There were 18 retained papers, reports or Web sites; 4 of these were professional national guidelines. All but 1 reference cited serum creatinine or estimated glomerular filtration rate as a criterion for referral. Other referral criteria were proteinuria (8 sources), blood pressure (5 sources), electrolytes (3 sources) or hematuria (3 sources). There was inconsistency in referral recommendations. Conclusions: The differing advice identified in the literature results in confusion as to when patients should be referred to a nephrologist. Nephrologists, an already strained human resource, must prioritize requests for consultation using an undefined and no doubt inconsistent metric. Standardized, diagnosis neutral criteria would benefit both primary care providers and nephrologists.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.027 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".