Review of: "Hospitalizations and emergency department visits trends among elderly individuals in proximity to death: a retrospective population-based study"
Bibliographic record
Abstract
Potential competing interests: The author(s) declared that no potential competing interests exist.The authors have performed a secondary analysis of the electronic health records of individuals aged 65 and above, living in the Friuli-Venezia Giulia region of northeastern Italy, covering the period from 2000-2014, investigating hospitalizations and emergency department visits (EDV) two years preceding death in people (n=142,834) who died between 2000 and 2014.Death was defined using death certificates.Residents who were "not continuously registered" in the region during the two years before dying (n=19,291 individuals) were excluded.Researchers estimated the percentage of one or more hospitalization or EDV two-year preceding death, stratified results by age group [65-74 years], [75-84 years], [85-94 years], and [>=95 years]), by sex, the leading cause of death (cancer, cardiovascular disease, respiratory disease).They also reported on other outcomes, including the duration of hospitalizations (please refer to the outcome section).Authors reported that (1) percentage of individuals accessing [acute?] healthcare services increased exponentially in proximity to death (hospitalizations = 4.7 months, EDVs = 3.9 months before death); this was inversely related to age, with changes among the youngest and eldest decedents at 6.6 and 3.5 months for hospitalizations and at 4.6 and 3.3 months for EDVs, respectively; (2) healthcare use among individuals with cancer increased "earlier in life" (hospitalizations = 6.8,EDVs = 5.8 months before death);(3) individuals with respiratory diseases were most likely to access hospital-based services during the last month of life; and (4) no sex-based differences were found.Researchers concluded that "greater use of acute healthcare services among younger individuals and cancer patients suggests that policies potentiating primary care support targeting these at-risk groups may reduce pressure on hospital-based services."The strength of the data utilized in this study includes its population relevance and the ability to examine a range of hospitalizations and EDV, covering approximately 2% of the population of Italy (i.e., 1.2 mil people live in the Friuli-Venezia Giulia region).Likewise, there was a possibility to compare individuals across the age categories and biological sexes and the cause of death.The results are important for surveillance and public health.Below are some points that limit appreciation of the study results as they concern study methodology and reporting:
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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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".