Adolescent and young adult health in a children's hospital: Everybody's business
Bibliographic record
Abstract
BACKGROUND: To guide the development of adolescent health training and the planning of future services, accurate data describing health service use by adolescents and young adults are needed. AIM: To describe admission rates for adolescents (12-17 years) and young adults (age 18 years and over) attending a specialist children's hospital over an 8-year period. Specific objectives were to describe the (i) proportion of adolescents and young adults admitted under different specialties; (ii) age range, with emphasis on those 18 years and over; and (iii) proportion of patients admitted to the general adolescent ward. METHODS: Data on adolescent and young adult admissions to Princess Margaret Hospital (PMH) were collected prospectively from July 2000 to June 2008. RESULTS: Adolescents and young adults accounted for one fifth (range 18-22%) of all admissions to PMH. Over the 8-year period, the number of adolescent and young adult admissions increased from 3935 (54% males) to 4967 (56% males) per year. The proportion admitted to the general adolescent ward ranged from 22% to 36%. The three specialties admitting the most adolescents and young adults were General Surgery (11-13%), Orthopaedics (11-13%) and Oncology/Haematology (10-14%). The age range was: 12-14 years (57-67%); 15-17 (30-39%); 18+ (2-5%). At least 15 patients aged 20 or over were admitted each year, mostly for Dental or Plastic Surgery. CONCLUSIONS: Adolescent and young adult health is part of the core business of paediatrics. This should be reflected in the planning of future paediatric services. All trainees require some basic training, regardless of heir specialty area.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".