Bibliographic record
Abstract
Imagine you have a choice. Tomorrow, you can either be dead, or you can choose a 1% chance of survival. For most of us, it's an easy decision; the odds of survival aren't good, but they are better than certain death. Now imagine that you are trying to persuade a patient not to receive cardiopulmonary resuscitation (CPR). ‘It almost certainly would not work, would only prolong the dying process,’ you explain. But CPR could work: there is perhaps a 1% chance of surviving to see another day. Why would patients choose not to receive it? However, despite most people's clear preference for continued life, the collective experience of clinicians is that CPR should not always be performed, and in many groups of patients, should only rarely be attempted. Individual physicians are required to respect this experience, while at the same time maintaining a high-quality doctor-patient relationship and providing realistic choices to seriously ill patients. The preference not to provide CPR in many circumstances exists for good reasons. I remember clearly my first night working in the emergency department as a second year medical student. A very frail man suffering from cardiac cachexia and severe COPD arrived soaked in sweat and labouring to breathe. He became unconscious as he entered the final stages of dying. ‘Do you want us to resuscitate him?’ the family was hurriedly asked in a back room, away from his suffering. ‘Yes,’ they replied. I remember his eyes as he looked about uncomprehendingly after he was intubated and regained consciousness. He died a few days later in the intensive care unit (ICU). Life sustaining technology is seductive. What was happening had seemed completely wrong, but this perception lessened when I took hold of the ambi-bag and breathed life back in his sodden lungs. Although patients may not …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".