Equipment in the Global Radiology Environment: Why We Fail, How We Could Succeed
Bibliographic record
Abstract
Purpose: This research aims to understand key problems and identify possible solutions in the market for radiology equipment in low- and middle-income countries. Methods and Materials: This paper uses simple descriptive statistics to summarize the results of responses from 574 radiologists from 52 countries surveyed in April-May 2017, and 15 hardware and software vendors from six countries surveyed in September-October 2017. Results: Radiologists surveyed came from both public and private sectors and were drawn from Radiological Society of North America (RSNA) members who, according to the survey results, appear to represent sites with more advanced technology. Virtually all the radiologists worked at sites where both X-ray and ultrasound were available, and the overwhelming majority (93%) had access to CT. Digital technology has gone worldwide: radiologists in all countries reported that digital radiography was either equally or more available than analog technologies. Sixty percent of radiologists said that they were “always” or “often” involved in the purchasing decisions in their institutions, but only 35% reported that they had the final say. According to the radiologists surveyed, the era of donated equipment is ending. Ninety-five percent felt that the disadvantages of donated equipment outweighed the cost savings. Training was a key concern both for radiologists and vendors. Radiologists felt that training was insufficient, materials left behind too complicated, online materials too limited, and follow-up from vendors insufficient. Vendors pointed out that the bidding process often excluded the cost of training and support and that many purchases are made through local distributors and they lack direct contact with vendors. Conclusion: While digital radiology is spreading throughout the surveyed countries, access to advanced imaging remains limited. Donated equipment is no longer a major solution to limited equipment availability. There is an opportunity for vendors and radiologists to work together to ensure that training, service and support are always included in purchases.
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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 | 0.031 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.022 | 0.034 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".