The spectrum of rheumatic in-patient diagnoses at a pediatric hospital in Kenya
Bibliographic record
Abstract
BACKGROUND: Pediatric rheumatic diseases are chronic illnesses that can cause considerable disease burden to children and their families. There is limited epidemiologic data on these diseases in East Africa. The aim of this study was to assess the spectrum of pediatric rheumatic diagnoses in an in-patient setting and determine the accuracy of ICD-10 codes in identifying these conditions. METHODS: Medical records from Gertrude's Children's Hospital in Kenya were reviewed for patients diagnosed with "diseases of the musculoskeletal system and connective tissue" as per ICD-10 diagnostic codes assigned at discharge between January and December 2011. Cases were classified as "rheumatic" or "non-rheumatic". Accuracy of the assigned ICD-10 code was ascertained. Death records were reviewed. Longitudinal follow-up of "rheumatic" cases was done by chart review up to March 2014. RESULTS: Twenty six patients were classified as having a "rheumatic" condition accounting for 0.32% of patients admitted. Of these, 11 (42.3%) had an acute inflammatory arthropathy, 6 (23.1%) had septic arthritis, 4 (15.4%) had Kawasaki disease, 2 (7.7%) had pyomyositis, and there was one case each of septic bursitis, rheumatic fever, and a non-specific soft tissue disorder. No cases of juvenile idiopathic arthritis (JIA) were identified. One case of systemic lupus erythematosus was documented by death records. The agreement between the treating physician's discharge diagnosis and medical records ICD-10 code assignment was good (Kappa: 0.769). On follow-up, one child had recurrent knee swelling that was suspicious for JIA. CONCLUSIONS: Pediatric rheumatic conditions represented 0.32% of admissions at a pediatric hospital in Kenya. Acute inflammatory arthropathies, septic arthritis and Kawasaki disease were the most frequent in-patient rheumatic diagnoses. Chronic pediatric rheumatic diseases were rare amongst this in-patient population. Despite limitations associated with the use of administrative diagnostic codes, they can be a first step in evaluating the spectrum of pediatric rheumatic conditions in Kenya and other countries in East Africa.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".