Use of a Telephone Nursing Line in a Pediatric Neurology Clinic: One Approach to the Shortage of Subspecialists
Bibliographic record
Abstract
OBJECTIVE: There are not enough pediatric neurologists to meet the many needs of pediatric neurology patients. The Hospital for Sick Children has responded by expanding the nursing role in the pediatric neurology outpatient clinic. The objective of this study was to examine the use of a telephone nursing line in this hospital-based pediatric neurology clinic. METHODS: A cross-sectional study was performed on all telephone call records collected during a 2-week study period. Each initial incoming call concerning a patient was counted as an index call. Associations between clinic type or diagnosis and length of telephone calls were assessed using the chi(2) test. RESULTS: A total of 208 index calls were received, generating a total of 597 incoming and outgoing calls. The most common clinic types were Epilepsy clinic (35.6%) and General Neurology clinic (32.7%), and the most common patient diagnoses were epilepsy (63.5%) and developmental delay (45.2%). Most patients were between the ages of 1 and <7 years (33.9%) and 12 and <18 years (32.8%) and male (55.2%). Most calls were made by mothers (57.2%) to ask about medical administrative issues (28.4%) and/or symptoms (27.9%). Physicians were notified for 47.1% of calls; nurses were twice as likely to notify physicians for calls concerning new symptoms (relative risk: 2.1; 95% confidence interval: 1.6-2.7). Most calls required between 1 and 5 minutes (49.0%). Long telephone calls (>10 minutes) were strongly associated with a diagnosis of epilepsy. CONCLUSIONS: There is a high demand for the neurology nursing line in our clinic. Most telephone calls and most long telephone calls concerned patients with epilepsy. Nurses managed more than half of all telephone calls without physician assistance. Use of a nursing line can aid in the provision of care to complicated subspecialty patients. Additional strategies are needed to optimize delivery of care to high-need medical populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".