‘Cognitive biases plus’: covert subverters of healthcare evidence
Bibliographic record
Abstract
The evidence-based medicine (EBM) paradigm has been associated with many benefits, but there have also been ‘some negative consequences’. In part, the consequences may be attributable to: (1) limitations in some of the tenets of EBM, and (2) flawed or unethical decisions in healthcare related organisations. We hypothesise that at the core of both is a cascade of predominantly unconscious cognitive processes we have syndromically termed ‘cognitive biases plus’, with conflicts of interest (CoIs) as crucial elements. CoIs (financial, and non-financial including intellectual) catalyse self-serving bias and a cascade of other ‘cognitive biases plus’ with several reinforcing loops. Authority bias, herd effect, scientific inbreeding, replication publication biases, and ethical violations (especially subtle statistical), are key contributors to the cascade; automation biases through uncritical use of statistical software and applications (apps) of preappraised sources of evidence at point of care, may be other increasingly important factors. The ‘cognitive biases plus’ cascade which involves several intricately connected healthcare-related organisations has the potential to facilitate, compound and entrench flaws in the paradigm, evidence and decisions that converge to inform person-centered healthcare. Our reasoning is based on observational data and opinion. However, the susceptibility of all humans to ‘cognitive biases plus’ makes our hypothesis plausible. Individual and collective fallibility may be minimised and the quality of healthcare decisions (including those related to improving EBM) enhanced by being conscious of our vulnerability and open-minded to the ‘outside view’.
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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 | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Commentary 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 | 0.136 | 0.360 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.004 | 0.052 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".