Clinical anisotropy: A case for shared decision making in the age of too much data and patient dis‐integration
Bibliographic record
Abstract
Today, in the age of big data, we are more capable than ever before. But even having the world at our disposal with naught but the touch of a button, we find ourselves exceedingly vulnerable in the patient chair. With insurmountable amounts of knowledge being published and disseminated around the world, how can clinicians keep up and what can be done about it? And sitting in the patient chair, bewildered by the ever-changing landscape of medicine at the blink of an eye, how can we, as patients, ever hope to be part of the conversations revolving around our own health? In this work, we explore the present-day problems of big data in the clinical context, how failing to integrate patients can result in detrimental outcomes, and what shared decision making can do about it.
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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 | 0.396 | 0.517 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.035 | 0.123 |
| Scholarly communication | 0.057 | 0.062 |
| Open science | 0.012 | 0.074 |
| Research integrity | 0.033 | 0.063 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".