‘Cognitive biases plus’: covert subverters of healthcare evidence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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’.
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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it