Recommended Guidelines for Monitoring, Reporting, and Conducting Research on Medical Emergency Team, Outreach, and Rapid Response Systems: An Utstein-Style Scientific Statement
Bibliographic record
Abstract
T he majority of patients hospitalized with a cardiac arrest or requiring emergency transfer to the intensive care unit have abnormal physiological values recorded in the hours preceding the event.[1][2][3][4][5][6][7][8][9][10][11] Many studies document that physiological measurements often are not made or recorded during this critical time of clinical deterioration.[12][13][14][15][16] Such physiological abnormalities can be associated with adverse outcome.[17][18][19][20] Measurements of abnormal physiology, including temperature, pulse rate, blood pressure, respiratory rate, hemoglobin, oxygen saturation by pulse oximetry, and deterioration of mental status, are therefore important to any system designed for early detection of physiological instability.At a minimum, these measurements must be obtained accurately and recorded with appropriate frequency.A system that both recognizes significantly abnormal values and triggers an immediate and appropriate treatment response is required.The American Heart Association makes every effort to avoid any actual or potential conflicts of interest that may arise as a result of an outside relationship or a personal, professional, or business interest of a member of the writing panel.Specifically, all members of the writing group are required to complete and submit a Disclosure Questionnaire showing all such relationships that might be perceived as real or potential conflicts of interest.
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 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 | 0.071 | 0.257 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.016 | 0.005 |
| Research integrity | 0.034 | 0.021 |
| Insufficient payload (model declined to judge) | 0.019 | 0.017 |
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".