Regulation, approval, and access of spinal implants in low-middle-income countries: a narrative review and case study
Bibliographic record
Abstract
INTRODUCTION: Spinal implants play a vital role in healthcare delivery, and regulations are necessary to ensure their quality, approval, access, and use. In this article, we examine the current state of regulation and approval procedures for medical devices in low- and middle-income countries (LMICs), emphasizing the situation in Tanzania. AREAS COVERED: We conducted a systematic literature search and interviewed a local spine implant representative to investigate the approval, availability, and access of surgical and spinal implants in LMICs, particularly in Africa. Out of the 18 included articles, six referred to African regulations, with no mention of spinal implants. Our analysis revealed that LMICs face challenges in accessing implants due to affordability, poor supply chain, and lack of expertise for their application. However, surgeons have found alternative solutions, such as using lower-cost implants from Turkish manufacturers. The Tanzania Medical Devices and Drugs Authority oversees the local regulatory and approval process for implants. EXPERT OPINION: Regulation and accessibility of spinal implants in LMICs, particularly in Africa, are limited and negatively impact patient care and best medical practice. Potential solutions include capacity building within and collaboration among regulatory organizations to improve regulatory processes and allocating financial resources to qualitative and quantitative implant access.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".