Prolonging life and delaying death: The role of physicians in the context of limited intensive care resources
Bibliographic record
Abstract
Critical care is in an emerging crisis of conflict between what individuals expect and the economic burden society and government are prepared to provide. The goal of critical care support is to prevent suffering and premature death by intensive therapy of reversible illnesses within a reasonable timeframe. Recently, it has become apparent that early support in an intensive care environment can improve patient outcomes. However, life support technology has advanced, allowing physicians to prolong life (and postpone death) in circumstances that were not possible in the recent past. This has been recognized by not only the medical community, but also by society at large. One corollary may be that expectations for recovery from critical illness have also become extremely high. In addition, greater numbers of patients are dying in intensive care units after having receiving prolonged durations of life-sustaining therapy. Herein lies the emerging crisis -- critical care therapy must be available in a timely fashion for those who require it urgently, yet its provision is largely dependent on a finite availability of both capital and human resources. Physicians are often placed in a troubling conflict of interest by pressures to use health resources prudently while also promoting the equitable and timely access to critical care therapy. In this commentary, these issues are broadly discussed from the perspective of the individual clinician as well as that of society as a whole. The intent is to generate dialogue on the dynamic between individual clinicians navigating the complexities of how and when to use critical care support in the context of end-of-life issues, the increasing demands placed on finite critical care capacity, and the reasonable expectations of society.
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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 | 0.014 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.044 | 0.037 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".