Waiting times in a tertiary paediatric nephrology clinic
Bibliographic record
Abstract
BACKGROUND: To the authors' knowledge, paediatric nephrology waiting times have not been previously studied. Given the high incidence of new referrals each year, the evaluation of the current waiting times would be beneficial in the management and triaging of new appointments. PATIENTS AND METHODS: Using descriptive statistics, data from all appropriate paediatric nephrology referrals to the Children's Hospital of Eastern Ontario (Ottawa, Ontario) from 2003 to 2005 (n=1446) were retrospectively analyzed. RESULTS: The median waiting time from receipt of initial request for referral to first appointment was 111 days (range zero to 364 days). No significant variation existed throughout the duration of the study, despite the variation in the number of paediatric nephrology staff. Infants were seen significantly sooner than older children. There were no assigned priority classification levels based on referral reason. Critical conditions, such as macrohematuria, were seen on an urgent basis; all other patients were seen at the next available appointment slot, which was usually four months away. A significant proportion of patients were referred for dysfunctional voiding and enuresis (25.9%). These diagnoses are not generally considered a part of core nephrology. CONCLUSION: The waiting times for a paediatric nephrology appointment are long. Focusing on core nephrology business and appropriate triaging of consult would be necessary to implement a priority classification level-based appointment assignment. Additional resources would allow for more patients to be seen in a more timely fashion.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".