An Investigation of Satellite Hemodialysis Fallbacks in the Province of Ontario
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In Ontario, Canada, hemodialysis services are organized in a "hub and spoke" model comprised of regional centers (hubs), satellites, and independent health facilities (IHFs; spokes). Rarely is a nephrologist on site when dialysis treatments take place at satellite units or IHFs. Situations occur that require transfer of the patient back ("fallbacks") to the regional center that necessitate either in- or outpatient care. Growth in the satellite dialysis population has led to an increased burden on the regional centers. This study was carried out to determine the incidence, nature, and outcome of such fallbacks to aid resource planning. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Data were collected on 565 patients from five regional centers over 1 yr. These regional centers controlled 19 satellite dialysis centers including 7 IHFs. RESULTS: There were 681 fallbacks in 328 patients: 1.21 incidents per patient or 2.1 incidents per patient year. Multiple fallbacks occurred in 170 patients. Fallback episodes lasted a mean of 10.3 d, requiring 4.6 dialysis treatments. Forty-five percent of fallbacks required hospitalization with a mean stay of 16.7 d. Access-related problems (33%) and nondialysis medical causes (32%) were the major causes of fallback. Resolution of the problem occurred in 87.8%, with the patient returning to the satellite. By the end of the study 77.3% were still satellite patients, 10.8% died, 3.8% returned to the regional center, 3.4% were transplanted, and 4.7% were transferred to other treatment modalities. CONCLUSIONS: Fallbacks are common, yet the model operates well.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".