Global Cost and Availability of Neuro-Diagnostic Tests: A Survey-Based Analysis (S37.004)
Bibliographic record
Abstract
Objective: To assess the global availability and cost of five neuro-diagnostic tests (CT, EEG, EMG, MRI, and lumbar puncture) to better characterize challenges in the diagnosis of neurologic diseases in resource-limited settings. Background: While the global distribution of neurologists and access to therapeutic options has been examined, less is known about access to neuro-diagnostic tests. Methods: We designed a 40-question survey assessing the availability and cost of CT, EEG, EMG, MRI, and LP. We then distributed the survey electronically to neurologists using a global listserv. Results: A total of 54 responses were received from 32 unique countries; 22[percnt] (7/32) of respondents were from low-income countries (LICs), 19[percnt] (6/32) from lower-middle income countries (LMICs), 34[percnt] (11/32) from upper-middle income countries (UMICs), and 25[percnt] (8/32) from high-income countries (HICs). The availability of tests in LICs, LMICs, UMICs, and HICs (respectively) were: 71[percnt], 80[percnt], 91[percnt], 100[percnt] for EEG; 71[percnt], 100[percnt], 100[percnt], 100[percnt] for CT; 57[percnt], 67[percnt], 82[percnt], and 88[percnt] for MRI; and 71[percnt], 80[percnt], 91[percnt], 100[percnt] for EMG. The average costs of diagnostic tests in LIC, LMIC, UMIC, and HIC (respectively) were: US$21.76, US$15.00, US$12.95, US$18.86 for EEG; US$89.03, US$72.27, US$18.13, US$36.19 for CT; US$206.14, US$87.22, US$108.27, US$176.61 for MRI; US$31.46, US$37.50, US$72.32, US$27.17 for EMG; US$12.10, US$13.40, US$34.88, US$32.63 for LP. The tests most often listed as “necessary but not available” were EEG and CSF analysis in LICs, and EEG and genetic testing in LMICs. Conclusion: Access to neuro-diagnostic tests is limited in LICs and LMICs; cost is often a significant barrier. With improved access to diagnostics, care for patients with neurologic diseases can be improved and access to therapeutics optimized. Improving the availability of neuro-diagnostic tests is important in both the evaluation and treatment of individual patients, and it is essential in order to characterize the global epidemiology of neurologic diseases.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".