© 2010 Canadian Medical Association or its licensors
Bibliographic record
Abstract
And that’s not all... I wholeheartedly concur with Chorley, McGrath and Finlay, and their observa-tions and conclusions about the pain associated with treatment that we inflict on our patients.1 I would add a similar consequence of our treatment: habitua-tion and addiction to the narcotics we inflict on some patients whose pain we try to alleviate. Often the pain that we first believe is acute transpires to be chronic. Once the dose of a drug has been titrated in pursuit of a level of relief that seems to become ever more elusive, the patient develops numerous adverse symptoms, including hyperes-thesia, constipation, hyperhydrosis and endocrine disorders, such as testos-terone suppression. Habituation often leads to addiction. But when we awaken to the situation, we are con-fronted with the prospect of having to induce most uncomfortable withdrawal symptoms. Who then can blame us for occasionally turning a blind eye to patients in pain rather than risk such an outcome?
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.811 | 0.713 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".