Evidence-based medicine targets the individual patient, part 1: how clinicians can use study results to determine optimal individual care
Bibliographic record
Abstract
Despite increasing acknowledgement of its importance,1 some continue to characterise evidence-based medicine (EBM) as focusing on groups of patients and neglecting the individual.2 3 In this 2-part commentary, we will describe EBM tools that address individual patient decision-making. In this first part we will focus on guides for applying research evidence and for determining the benefit to risk ratio in individual patients. EBM assists clinicians pondering the generalisability of RCT results to their individual patients, and their individual circumstances (table 1).4-6 That guidance directs clinicians to look for possible differences in biological factors, socioeconomic characteristics, and attitudinal or behavioural characteristics of individual patients that might modulate treatment effects.7 For instance, antibiotics for otitis media seem to be most beneficial in children younger than 2 years of age with bilateral acute otitis media, and in children with both acute otitis media and otorrhoea. …
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.352 | 0.643 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.029 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".