Sources of delayed provision of neurosurgical care in a rural kenyan setting
Bibliographic record
Abstract
BACKGROUND: Delay to neurosurgical care can result in significant morbidity and mortality. In this study, we aim to identify and quantify the sources of delay to neurosurgical consultation and care at a rural setting in Kenya. METHODS: A mixed-methods, cross-sectional analysis of all patients admitted to the neurosurgical department at Kijabe Hospital (KH) was conducted: A retrospective analysis of admissions from October 1 to December 31, 2013 and a prospective analysis from June 2 to June 20, 2014. Sources of delay were categorized and quantified. The Kruskal-Wallis test was used to identify an overall significant difference among diagnoses. The Mann-Whitney U test was used for pairwise comparisons within groups; the Bonferroni correction was applied to the alpha level of significance (0.05) according to the number of comparisons conducted. IBM SPSS version 22.0 (SPSS, Chicago, IL) was used for statistical analyses. RESULTS: A total of 332 admissions were reviewed (237 retrospective, 95 prospective). The majority was pediatric admissions (median age: 3 months). Hydrocephalus (35%) and neural tube defects (NTDs; 27%) were most common. At least one source of delay was identified in 192 cases (58%); 39 (12%) were affected by multiple sources. Delay in primary care (PCPs), in isolation or combined with other sources, comprised 137 of total (71%); misdiagnosis or incorrect management comprised 46 (34%) of these. Finances contributed to delays in 25 of 95 prospective cases. At a median delay of 49 and 200.5 days, the diagnoses of hydrocephalus and tumors were associated with a significantly longer delay compared with NTDs (P < 0.001). CONCLUSION: A substantial proportion of patients experienced delays in procuring pediatric neurosurgical care. Improvement in PCP knowledge base, implementation of a triage and referral process, and development of community-based funding strategies can potentially reduce these delays.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".