Children who ‘grow up’ in hospital: Inpatient stays of six months or longer
Bibliographic record
Abstract
OBJECTIVE: To describe the clinical course of all infants and children hospitalized for six consecutive months (180 days) or longer at a tertiary/quaternary children's hospital in Western Canada. METHODS: A retrospective review of medical records for all eligible patients from January 1, 2007 to December 31, 2012 at Stollery Children's Hospital (Edmonton, Alberta) was performed. RESULTS: A total of 61 patients experienced 64 eligible hospitalizations. The mean length of stay was 326 days, corresponding to a cumulative 20,892 hospital days (57.2 patient-years). Prevalent procedures resulting in long hospitalization were long-term tracheostomy ± ventilation in 32 (52%) patients, need for organ transplantation in 24 (39%) with completed transplantation in 15 (25%), and ventricular-assist devices (VADs) in seven (11%). Sixteen (26%) patients in the study group died, and 16 (26%) were placed in long-term care or out-of-home care at the end of their long hospitalization. Of children displaced from their family home, 14 (88%) were Aboriginal. CONCLUSION: Infants and children who experience very long hospitalizations have complex illnesses, with substantial risk for mortality and a high rate of displacement from their families after discharge. Aboriginal children appear to be particularly vulnerable to displacement and problem solving for this population must be undertaken, involving a variety of stakeholders.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".