Mortality in Paediatric Medical Services over a Two-Year Period at the Georgetown Public Hospital Corporation
Bibliographic record
Abstract
Objectives: To identify common causes and patterns of mortality and associated modifiable factors in Paediatric medical services, GPHC for 2016 and 2017. Design and Methods: Descriptive, Cross-sectional retrospective study of all Paediatrics deaths for 2016 and 2017 from the Paediatrics Medical Ward and ICU at GPHC. Data were analysed using IBM SPSS version 23 for descriptive statistics. Results: The Paediatrics Medical Ward saw 2419 admissions in 2016 and 2168 in 2017. There were 86 deaths recorded in the death registry during the two years, of which 57 deceased patient charts were reviewed, 29 charts (33.7%) were missing. Of the 57 charts reviewed, 42.1% were males and 57.9% were females. The most common final diagnosis was bronchopneumonia/pneumonia (n=1 (22.8%), followed by sepsis (n= 12 (21.0%). This pattern was seen in both the general sample population and the patients ≤ 5 years. Some of the modifiable factors which showed clinical significance when compared with length of hospitalization were; lack of routine reviews on the ward (p value 0.016), poor documentation of consultation information on referral notes (p value 0.032) and poor or no pre-hospital treatment before transfer (p value 0.017). Conclusions: Factors associated with mortality are mainly hospital-related and should, therefore, be given urgent attention for resolution which may reduce childhood mortality. In addition, the common causes of death are preventable and treatable respiratory and infectious diseases. Recommendations: Develop referral, transfer and management protocols for critical patients. Sensitization of staff on modifiable factors to effect positive behavioural change.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".