Necessity of Diagnostic Imaging and its Lacking Availability
Bibliographic record
Abstract
The World Health Organization (WHO) states that 50-75% of people across the globe do not have adequate access to basic diagnostic imaging such as ultrasound and X-ray. Studies have shown that ultrasound and X-ray can address 80-90% of imaging needs in developing countries. A proper understanding of what access to diagnostic imaging means is important to provide equitable and effective solutions. It is not enough to be geographically located near a hospital that has diagnostic imaging, but the imaging must also address the local need, be affordable to the country, and be scientifically valid. No matter the location, whether it be in developed or developing countries, access to diagnostic imaging is a necessity and a problem that needs a solution. Potential solutions exist such as the WHO’s technical review titled “The Needs Assessment for Medical Devices” which demonstrates how to calculate gaps in access to medical technologies specifically by cataloguing what type of diagnostic imaging is available and what should be available. Other solutions include non-profit organizations, such as “RAD-AID”, which are working towards increasing radiology services in developing countries. Finally, donating diagnostic imaging devices is another potential solution to address the need for this technology in places that lack it. However, there are many important factors to consider before making a donation. Overall, diagnostic imaging, such as X-ray and ultrasound, have an important role in patient care and are needed in countries that cannot access any other imaging modalities.
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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 | 0.017 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".