An audit of cancer diagnosis in a Canadian children's hospital: Quality, timing and efficiency
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The diagnosis of paediatric cancer requires multidisciplinary cooperation to achieve both a timely diagnosis and efficient resource use. The authors undertook a 12-month audit of paediatric cancer cases to assess BC's Children's Hospital's (Vancouver, British Columbia) diagnostic process from the perspective of quality (timing and accuracy of diagnosis) and procedural efficiency, with an emphasis on the impact on resource use in the departments of radiology, pathology, anesthesia and surgery. METHODS: Malignancies (excluding brain and cortical bone primary tumours, for which the preoperative diagnostic workup is often completed before admission) diagnosed between January 1 to December 31, 2003, were reviewed. Data collected included total outpatient versus inpatient procedures, number and timing of diagnostic procedures, general anesthesia (GA) requirements, and lag times from admission to biopsy to diagnosis during the initial hospitalization. RESULTS: Fifty-four patients were identified. Only 10 patients (19%) had an outpatient diagnostic procedure. One hundred seventeen inpatient diagnostic procedures were performed, with only 50% occurring within regular working hours. Thirty-one per cent of patients required two or more procedural GAs during their initial hospital admission. The mean lag time to biopsy was 2.6 days and to a pathological diagnosis was 1.2 days. CONCLUSIONS: Despite timeliness, the process of cancer diagnosis at BC Children's Hospital requires hospital admission and a significant consumption of resources outside of regular working hours. Opportunities for improvement include maximizing outpatient workup, allocating oncology operating room time to increase the percentage of weekday procedures and improving interdisciplinary procedural coordination to reduce the GA requirements per patient.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".