Multidetector computed tomography utilization in an urban sub-Saharan Africa setting: user characteristics, indications and appropriateness
Bibliographic record
Abstract
INTRODUCTION: multidetector computed tomography (MDCT) is a widely used cross-sectional imaging modality despite increasing concerns about radiation exposure and overuse. The aim of this study was to describe the socio-demographic characteristics of MDCT users in an urban city in Cameroon and to assess the clinical indications for appropriateness. METHODS: we conducted a survey of MDCT users and collected data on demographic attributes and socialization patterns, clinical indications for MDCT and time to obtain MDCT. MDCT appropriateness was assessed using the American College of Radiologists Appropriateness Criteria®. Frequencies, percentages, odds ratios and 95% confidence intervals were used to summarize the data. RESULTS: with a response rate of 79%, 511 MDCT users were surveyed. The mean (standard deviation) age was 45(19) years and male to female sex ratio 1:1. Seventy-eight percent (95% confidence interval [CI]: 74-83%) of respondents reported not having any health insurance. Head scans accounted for 52% (95%CI: 47-56%) of all scans with trauma (19% [95%CI: 15-22%]), low back pain (18% [95%CI: 14-21%]) and suspected stroke (10% [95%CI: 7-13%]) being the most frequent indications. Sixteen percent (95%CI: 13-20%) of the scans were judged to be inappropriate. Predictors of MDCT appropriateness after multivariable logistic regression modeling were age (aOR=0.97; P=0.009; 95%CI=0.94-0.99), health insurance ownership (aOR=0.40; P=0.034; 95%CI=0.18-0.94) and being referred by non-specialist physicians (aOR=0.20; P<0.001; 95%CI=0.09-0.47). CONCLUSION: people from all social strata use MDCT, mostly appropriately and especially for head scans after trauma in this urban setting. However, the proportion of inappropriate studies was considerable suggesting the need for control measures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".