The process of evidence‐based medicine and the search for meaning
Bibliographic record
Abstract
BACKGROUND AND RATIONALE: Evidence based medicine is the present backbone of rational and objective, modern medical problem solving and is a meeting ground for quantitative and qualitative researchers alike as it culminates into applying the fruits of clinical research to the individual patient. A systematic enquiry into the evolving paradigms in EBM is a need of the hour. AIMS AND METHODS: A qualitative enquiry examining the impact of different methodologies in EBM and their role in generating meaning interpretable at individual levels. RESULTS: Present day outcome based research deals less with patients as individuals than as populations. Evidence based medicine struggles to apply the fruits of population based research to individuals who are often not as predictable as linear quantitative research would like them to be. The present EBM literature neglects a lot of events it doesn't believe to be statistically significant and perhaps here is an area that needs to be improved on - it assumes that because associations are demonstrated between interventions and outcomes in RCTs/meta-analysis, these associations are linear and causal in the real world. While they may be demonstrated repeatedly in highly controlled environments, in the real 'uncontrolled' world of clinical practice with real people, their validity breaks down. CONCLUSIONS: One needs to make the EBM standard model patient-individual (a projection of collective patient event data) resemble the real human individual patient so that optimal EBM individual data that matches our query can be easily and quickly spotted from the dense jungle of information that has grown over the years. This hints at rethinking our entire research methodology and modifying it to suit the needs of the individual patient.
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 | 0.367 | 0.347 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.009 | 0.163 |
| Scholarly communication | 0.040 | 0.045 |
| Open science | 0.009 | 0.025 |
| Research integrity | 0.024 | 0.041 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".