India and end of life discussions: A comment on end of life discussion in an academic family health team in Kingston, Ontario, Canada
Bibliographic record
Abstract
Dear Editor, This is in regard to the article, “End of Life (EOL) Discussion in an Academic Family Health Team in Kingston, Ontario, Canada” published in Journal of Family Medicine Primary Care (2013;2:263-5). The authors have done a commendable job in exploring the prevalence of EOL discussions in non-terminal adult patients, the perceived barriers to such discussions and suggested methods for improvement.[1] The topic is of major importance given the growing concerns on end of line discussions, more so in our setup. We would like to make a few suggestions in this regard. Firstly, EOL discussions in terminally ill-patients should receive a major attention from not just the researchers but policy makers as well. Conducting EOL discussions with terminally ill-patients needs impetus as they are in greater need for the same. Research in the past has also shown that physicians can be poor prognosticators, frequently overestimating the life expectancy of a patient resulting in patients dying without the support of hospital, or due to the late nature of the referral, not realizing the full services that hospital are committed to provide.[2] Therefore, a guideline based protocol needs to be worked out for this purpose detailing the roles and responsibilities of different health care providers on EOL discussions. Many a times, EOL is avoided simply because of the non-availability of the treating physician at that point of time hence nursing staff also needs to be trained for the same. The timing of EOL discussion is another factor that needs to be taken into account. In this direction, a workgroup can be constituted to develop protocols as educational tools in conjunction with other educational materials for the benefit of a terminally ill-patient. This would help make patients aware of their options for quality EOL care.[2]
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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 | 0.020 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.045 | 0.017 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.048 | 0.062 |
| Insufficient payload (model declined to judge) | 0.008 | 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".