Advance care planning
Bibliographic record
Abstract
Mrs. G is 63 years old and has no significant history of illness. She presents for a routine visit to her family physician. She has read newspaper articles about living wills and thought that this was something she ought to address, but had never taken it further. In the physician's waiting room, she sees a leaflet on advance directives and decides that today would be a good day to learn more about this. Mr. H is a 40-year-old man who was diagnosed 6 months ago with advanced glioblastoma multiforme, an incurable brain tumor. He presents to his oncologist with symptoms of early cognitive dysfunction. The physician considers what Mr. H should be told about advance directives. What is advance care planning? Advance care planning is a process whereby a patient, in consultation with healthcare providers, family members, and important others, makes decisions about his or her future healthcare (Teno et al ., 1994). This planning may involve the preparation of a written advance directive (Emanuel et al ., 1991). Completed by patients when they are capable, advance directives are invoked in the event that the patient loses decision making capacity. Advance directives may indicate what interventions patients would or would not want in various situations, and whom they would want to name as healthcare surrogates to make treatment decisions on their behalf.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.145 | 0.040 |
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".