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Global Cost and Availability of Neuro-Diagnostic Tests: A Survey-Based Analysis (S37.004)

2015· article· en· W1577495277 on OpenAlexaff
Hannah McLane, Aaron L. Berkowitz, Emma Wolper, Erica McKenzie, Sarah Wahlster, Farrah J. Mateen

Bibliographic record

VenueNeurology · 2015
Typearticle
Languageen
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineGerontology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.006
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.024
GPT teacher head0.281
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations3
Published2015
Admission routes1
Has abstractyes

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