Bibliographic record
Abstract
For the assessment of diagnostic and therapeutic interventions a sound scientific base has been developed during the last twenty years. Under the headline of Evidence-based Medicine nowadays a comprehensive set of tools is offered which can be used to assess the benefit and the risk of medical interventions. The overarching rule which evolved for the grading of evidence from studies is to maximize the protection against bias. Despite this coherent approach, there is still controversy that is regularly mainly sparked by the dominant position of randomized controlled trials. Observational studies and registries are deemed to be more relevant because they provide results that are produced under "everyday conditions". These controversial discussions often show a lack of orientation, as they do without the explicit naming of scientific criteria for the evaluation and to a large extent rely on common sense. That the latter may not be a good guide for assessments in the medical field is known from numerous studies. For unbiased assessments the rigorous use of basic scientific principles is the only way. To express doubt and question these principles requires a scientific basis itself. The alternative is to move away from the established scientific foundation. The path to a "new" scientific paradigm is currently dominated by a discussion under the buzzword Big Data. Defined by the three V's of Variety, Velocity and Volume, a potential of the unlimited analysis of data is envisioned, for which there is currently no validation and whose logical foundations are extremely doubtful. The demand must be reaffirmed that instead of promises strict validation criteria be followed for the evaluation of all interventions in medicine, particularly in view of these developments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.062 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.048 |
| Scholarly communication | 0.012 | 0.036 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.026 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".