Bibliographic record
Abstract
Introduction: Critical Care Outreach Teams (CCOT) weredeveloped to improve care of the critically illpatients in areas of the hospital outside of theintensive care unit (ICU). The purpose of our studywas to explore the role of the CCOT team in endof life care at a Canadian tertiary care centre. Hypothesis: We hypothesized that CCOT would play a significant role in end of life discussions. Methods: A retrospective review was performed of patientsthat underwent CCOT assessment from January 1st2011 until June 30th 2011. Data was extractedfrom a prospectively collected CCOT database, aswell as patient charts. We sought to determinepatient code status, as well as initiation ofcode status discussions by the CCOT team. For thesubset of patients admitted to the ICU, data wascollected on the incidence of changes in codestatus once in the ICU as well as cause of death. Results: A total of 523 patients were identified forthe study period. CCOT was involved in initiatinga code status discussion in 6 patients (1.15%)and revisited the code status in 51 (9.77%) ofpatients. This was most frequent for patientswith no code status specified in the chart,followed by “No resuscitation”. 124 patients weretransferred to the ICU following CCOT assessment;100 patients were analyzed post admission to theICU. Of these 100 patients, 33 patients died with23 of these deaths as a result of withdrawal oflife support. Thirty patients had a change incode status, within a mean number of 6.8 days(SD 8.8) following admission to ICU. Conclusions: The majority of patients already had a codestatus established prior to CCOT involvement. Forthe subset of patients with the code status of”No Resuscitation” and “Code status notspecified”, CCOT played a larger role in the codestatus discussions. Once admitted to the ICU, themajority of patients had a change in code status,with a large number dying as a result ofwithdrawal of life support. Further data isneeded to establish the reason for the change incode status in the ICU and potentially avoidunnecessary ICU admissions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.629 | 0.467 |
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".