Open Access to a High-Quality, Impartial, Point-of-Care Medical Summary Would Save Lives: Why Does It Not Exist?
Bibliographic record
Abstract
Over the past decade, the world of scientific journal publishing has been transformed by open access (OA) to information.In the strict sense, open access refers to the ability for others not only to view but also to build upon and distribute a work as long as attribution of the author(s) is provided [1].PLOS ONE became the world's single largest journal (by number of articles) only four years after it was founded and has since increased in volume nearly 5-fold [2,3].Currently there are nearly 10,000 journals listed by the Directory of Open Access Journals; as of May 2015, more than 1.9 million articles [4].About a quarter of these were related to medicine.In 2011, of all scholarly articles published, 17% were OA [5], and in the biomedical fields the proportion of freely available articles (both OA and "free" access) passed the 50% mark in 2010 [6].The huge increase in access to scientific knowledge has been chiefly of benefit to those researchers who have the time to search the literature.It is less helpful for working health care providers, since masses of literature do not lend themselves to reliably and promptly answering questions.A recent estimate that 85% of all medical research is wasted is based on waste in the research process and in publishing the research itself [7].This problem of waste is compounded when medical knowledge exists but health care providers have to make decisions without it, a Summary Points• Currently no open access point-of-care (POC) medical summary aimed at a professional audience exists.• Some nonprofit and multiple professional, for-profit POC medical summaries are frequently accessed by clinicians and policymakers.• Efforts to create open access POC summaries have been stymied by the difficulty of attracting high-quality contributors.• The open access medical publishing community can create this resource with engaged donors, crowd-sourcing, and technology.
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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 | 0.034 | 0.205 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.042 | 0.017 |
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".