Unmet Surgical Need among Adults in A Mixed Urban-Rural Community in Nigeria: A Survey of 1,993 Adults Using the Sosas Survey Tool.
Bibliographic record
Abstract
INTRODUCTION: Community-based prevalence studies are known to be more accurate than hospital-based records. However, such community-based prevalence studies are uncommon in low- and middle-income countries including Nigeria. Allocation of resources and prioritization of health care needs by policy makers require data from such community-based studies to be meaningful and sustainable. This study aims to assess the prevalence of common surgical conditions amongst adults in Nigeria. METHODS: A descriptive cross-sectional community-based study to determine the prevalence of congenital and acquired surgical conditions in adults in a mixed rural-urban area of Lagos was conducted. The study population comprised resident members in the Ikorodu Local Government Area (LGA) of Lagos State. Data was collected using a modified version of the interviewer-administered questionnaire, the Surgeons OverSeas Assessment of Surgical Need (SOSAS) survey tool. Data was analysed using the REDCap analytic tool. RESULTS: Eight hundred and fifty-six households were surveyed with a yield of 1,992 adults. There were 95 adults who complained of surgical conditions giving a prevalence rate of 5%. Vast majority of reported conditions were acquired deformities (n=94) while only 1 congenital deformity was reported. Others included breast lumps, anterior neck swelling, and groin swellings. CONCLUSION: The most common surgical complaints in our setting among adults were acquired conditions of the extremities and open wounds/sores. With an estimated population of 90 million adults and approximately 1,200 orthopaedic and general surgeons respectively, the surgeon-to-affected population ratio is 1:10,000. There is a large gap to be filled in terms of surgical manpower development.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".