Clinical Research Survival From In-hospital Cardiac Arrest on the Internal Medicine Clinical Teaching Unit
Bibliographic record
Abstract
Background: There is a paucity of data on patient outcomes following in-hospital cardiac arrest (IHCA) on the Internal Medicine clinical teaching unit (CTU). Accurate outcome data enhances discussions between patients, surrogates, and physicians, and assists in their management. Methods: We performed a retrospective cohort study of consecutive “Code Blue” calls on 2 medical CTUs in a Canadian tertiary centre from January 1, 2003 to June 30, 2007. The medical records of identified patients were screened for eligibility and patient-specific and arrestspecific data were collected for eligible events. Primary outcome was survival to hospital discharge. Results: Our cohort comprised 83 patients; including 54 (65.1%) men with a mean age of 75 years (range, 38-97). Infection (34.9%) was the principal reason for admission and over half of patients had 3 or more comorbid illnesses. Forty-three (51.8%) of the IHCA events were witnessed. In all, 39 (90.7%) of the witnessed and 36 (90%) of the unwitnessed arrests were pulseless electrical activity (PEA) or asystole (P not significant). Return of spontaneous circulation occurred in 29 patients (34.9%) and 2 (2.4%) survived to hospital discharge. No patients survived to discharge after unwitnessed arrest. Conclusions: IHCA in Internal Medicine CTU patients is characterized by a high rate of PEA/asystole and a minimal chance of survival to
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".