Bibliographic record
Abstract
The Problem:From moment to moment, while tending their patients, physicians can slip from: patience to impatience; genuine enquiry to assumptions; attention to the task-at-hand to inattention; certain doubt to doubtful certainty; and from doing what is inconvenient to doing what is convenient. In summary, one can slip from neo-cortex to archi-cortex (reptilian brain) emphasis. (The hazardous attitudes associated with aviation and medical mishaps are reptilian in character). This is expressed: to err is human, the reptilian part.Objectives: Therefore, to improve decisions, one needs to reclaim the new-brain emphasis, the advocate for the patient’s interests.Method: To be aware which emphasis one commands, ask oneself a few reptilian-revealing questions. Then counter the reptilian attitude by specific and or generic antidotes to be less error prone.Conclusion: Doctors should realize that there is also another patient one is tending: the patient called oneself, whose symptoms are haste, egoism and apathy and whose diagnosis is the reptilian brain. While this lesion is inoperable and the prognosis is guarded, yet amid the uncertainty and demands of our medical tending, one can toggle back to patience and doing the inconvenient to reach after fact and reason.
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 | 0.006 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.036 | 0.025 |
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".