The Provision of Dialysis Services in \nRural and Remote Populations \nin Newfoundland and Labrador
Bibliographic record
Abstract
The province of Newfoundland and \nLabrador has the highest rate in the \ncountry of newly diagnosed patients \nover the age of 65 years with end-stage \nrenal (kidney) disease (ESRD).1 Here, \nas elsewhere in Canada, the profile of \npatients undergoing dialysis has changed, \nwith a growing number of older, and more \nmedically frail, \npatients being \noffered dialysis. \nIn January, 2008, \nthere were 380 \npatients on \ndialysis in this \nprovince, 65% of \nwhom were being treated by hemodialysis \nin main hospital-based dialysis units in St. \nJohn’s and Corner Brook, and in Grand \nFalls-Windsor, a satellite of St. John’s that \noperates much like a main unit. \nHospital-based hemodialysis is the \npredominant modality of dialysis in \nthis province despite the fact that it \nsometimes requires patients to travel \nlong distances or to relocate, and that \nother modalities of dialysis are available, \nincluding home-based and satellite-based \ndialysis services (see Table 1 on page 2). \nMaking decisions about the provision of \ndialysis services, particularly for rural and \nremote populations, poses challenges and \nshould be guided by research evidence. \nFurthermore, the evidence available \nfrom health technology assessments and \nsystematic reviews on dialysis must be \ninterpreted in light of the Newfoundland \nand Labrador context, taking into \naccount our aging population, our limited \nhuman and financial resources, and the \ngeographic dispersion of small clusters of \npatients with ESRD living in remote parts \nof the province. Providing health decision \nmakers with the best available evidence \nthat is attuned to the capacities and \ncharacteristics of the province is the goal \nof the Contextualized Health Research \nSynthesis Program (CHRSP).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.003 |
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".