Bibliographic record
Abstract
In Response: We appreciate the comments by Dr. Tabboush1 that pain has the potential to result in significant and pervasive effects on individuals. The research that Dr. Rashiq and I have carried out2 that was cited by Dr. Tabboush did find that chronic pain impacts cognitive function. Previous research has also supported that finding. There is little question that pain, particularly severe intractable pain, has the potential to impact an individual's ability to make informed choices. At the same time, we urge that great caution be used when considering the extent to which pain might invalidate an individual's ability to make choices. Cognitive disruption related to pain can have considerable effects on mental health, quality of life, and pain-related disability. However, the cognitive deficits noted in our research were, in our opinion, not severe enough that we would consider calling into question our participants' ability to make important life choices. The existing research on the effects of pain on cognition point toward a general trend in which cognition is most impaired when pain levels and the demands of the task being performed are high. Furthermore, there are a number of other factors that are known to influence pain's effects on cognition. We concur with Dr. Tabboush that this issue is complex and will require the cooperation of legal and clinical experts to adequately address it. Much remains to be learned regarding this issue that has important ethical, legal, and health policy implications. Bruce Dick, PhD Department of Anesthesiology and Pain Medicine University of Alberta, Edmonton Alberta, Canada [email protected]
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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 | 0.003 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.036 | 0.008 |
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".