How patients feel about prolonged mechanical ventilation 1 yr later*
Bibliographic record
Abstract
OBJECTIVES: To elicit mechanical ventilation preferences among patients who previously received prolonged (>/=48 hrs) mechanical ventilation, to identify patient characteristics associated with mechanical ventilation preferences, and to assess the association between the intensive care experience and mechanical ventilation preferences. DESIGN: Prospective cohort study conducted between June of 1997 and July of 2000. SETTING: Four intensive care units at a tertiary care institution. PATIENTS: Former critically ill patients (n = 133; mean age +/- sd, 51.8 +/- 17.1 yrs; 49% women) who survived for 12 months after prolonged mechanical ventilation. MEASUREMENTS: Patients' preferences toward their actual mechanical ventilation experiences, by asking patients to reflect on the decision to apply mechanical ventilation made 1 yr earlier. Preferences for hypothetical situations, by asking patients to evaluate mechanical ventilation choices, assuming that their experiences had been different in terms of pain or discomfort, familial financial burden and stress, and health status after mechanical ventilation. RESULTS: Of the 133 patients, 115 (86.5%) would have chosen mechanical ventilation, with younger and healthier patients having higher odds of choosing mechanical ventilation than older and sicker patients, respectively. One fourth of patients who initially would have chosen mechanical ventilation would have refused this therapy had their families' financial burdens been beyond certain thresholds. A similar proportion would have refused mechanical ventilation with greater mechanical ventilation pain or discomfort. CONCLUSION: Although most subjects would have made the same decision to receive mechanical ventilation, younger and healthier subjects were most likely to favor mechanical ventilation. Many patients indicated that factors such as the amount of pain or discomfort from mechanical ventilation and their families' financial burden would cause them to refuse this potentially life-saving intervention.
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 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 | 0.001 |
| 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) | 0.002 | 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".