Bibliographic record
Abstract
Evaluate MedTech estimated that worldwide sales of medical devices in 2017 were US$386.8 billion. Cardiology was among the largest groups, with $44.6 billion in sales.1 The Emergo Group estimated US sales to be $147.7 billion and sales in India to be $3.5 billion.2 The average annual growth of the device market since 2009 has been about 16%.3 Although many devices have been labelled as ‘single-use’ by the original manufacturers, some of these have nonetheless been reprocessed and used again. Some device manufacturers have warned against this practice,4 5 ostensibly because of the potential risks of infection or breakdown. For some time, hospitals have been reprocessing SUDs in-house. Also, since about the year 2000 a thriving third-party reprocessing industry has emerged in North America and Europe. Only about 2%–3% of all devices can be safely reprocessed.6 By 2016, global revenue of independent SUD reprocessors was estimated to be $1.054 billion.7 Estimated sales of third-party reprocessors in the USA was $848.5 million.8 In-house hospital activities are generally not included when considering the size of the reprocessing marketplace. In India, there is considerable in-house activity in device reprocessing in hospitals,9 10 but there is no large-scale SUD third-party market. We compare the difference in cost with the difference in harm between new and reused SUDs. The variables included in this comparison are shown in table 1. These variables are defined differently between countries because the markets and regulatory systems are so different. View this table: Table 1 Variables used for economic analysis of reuse in the USA and India The risk of harm is the major clinical outcome for reprocessing activity. To help regulate the safety of brand new and reprocessed devices, The US Food and Drug Administration (US FDA) developed a three-class licensing system.11 Devices in the …
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.004 |
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".