Mortalidad hospitalaria en un Servicio de Medicina Interna
Bibliographic record
Abstract
UNLABELLED: The Internal Medicine service of the Hospital General de Vic (Barcelona) takes part in the mortality committee by revising and discussing in-hospital mortality. BACKGROUND: to establish the characteristics of the deceased, death causes and to revise possible changes in the last six-years time or problems related to the exitus, to evaluate and improve hospitalized patientś assistance. METHODOLOGY: Every case was revised following a specific register: demographical data, diagnosis and death cause, hospital death, documentation data, terminal or agonic situation when hospitalized, autopsies and death quality data. Exitus due to hospital problems were analyzed and classified in different groups. The statistical analysis was performed with measures of central tendency and of standard deviation. RESULTS: During the revised six years, there were 819 exitus (5.1%). Global average death age was 79 +/- 1.8 years: 52.5% were men and 47.4% were women; 22.8% died in less than forty-eight hours after hospitalization. The most frequent death causes were cerebrovascular accident (24%), chronic obstructive pulmonary disease (14.4%) and pneumonia (9.6%). There were a small number of autopsies (4.8%). Ratio of exitus due to hospital problems was stable during the six years (0.5%), in which nosocomial infection was the severest problem. CONCLUSIONS: The total percentage of exitus was 5.1%, higher than the common standards. Mortality causes coincide with other series. Ratio of exitus due to hospital problems was according to recommended objectives. The number of autopsies was very small. A correct completing and revision of the clinical recording is indispensable to spot a shortage in the hospitalized patientś assistance.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".