We asked Chat GPT to describe brain fog in chronic pain: What did we learn?
Bibliographic record
Abstract
Chat GPT is a modern artificial intelligence program: its recent introduction has created controversy in the academic world. This commentary discusses the utility of Chat GPT to explore healthcare issues such as chronic pain and associated conditions. To illustrate the potential application of brain fog, this commentary presents an example of Chat GPT using brain fog. Brain fog is a phenomenon which has been increasingly discussed in both academic and social media discourses. Further, the potential advantages and dangers of Chat GPT are described. Noted advantages include search facilitation, drafting patient information, identifying opportunities for future research, and highlighting areas with a lack of consensus. Dangers of Chat GPT include the possibility for misinformation and the reproduction of social stereotypes and assumptions. Lastly, this commentary concludes with recommendations for healthcare professionals considering use of Chat GPT. Artificial intelligence-driven technologies like Chat GPT become increasingly available and trusted in our society, so does the importance of our awareness for both benefit and potential for harm when artificial intelligence is used for non-critical information seeking and self-education.
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.
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 | 0.011 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.022 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 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".