Coping with the CKD epidemic: the promise of multidisciplinary team-based care
Bibliographic record
Abstract
It is well known that late referral to a nephrologist is associated with many adverse outcomes [1–4], and indeed has been the subject of a recent review in this journal [5]. Some of the more important negative outcomes include more rapid onset of end-stage renal disease (ESRD), progression of co-morbid conditions such as anaemia and cardiovascular disease, suboptimal vascular access at initiation of dialysis, increased use of centre-based haemodialysis (HD), increased hospital utilization, increased cost and worse survival. The literature has many examples of suboptimal chronic kidney disease (CKD) care provided by primary care physicians prior to referral, and also shows clearly that care provided by nephrologists is better [6,7]. There is a consensus within the renal community that early referral is desirable [5,8–10].M There is much less consensus about how to provide CKD care if early referral is achieved. Multidisciplinary team-based care (MDC) is not a new idea and in fact was advocated by the NIH consensus group in the early 1990s [9]. Until now, it has been studied in a limited way, and with inconsistent results [11–14]. In this issue of the journal, Curtis and colleagues report that in an Italian and a Canadian setting, MDC was superior in several respects to standard care provided by a nephrologist [15]. Most impressively, they have documented an important survival advantage after initiation of dialysis. Recently, Goldstein and colleagues from Toronto, Canada have demonstrated a similar survival advantage with MDC [16].
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.046 | 0.043 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".