Clinical complexity and hospital admissions in the December holiday period
Bibliographic record
Abstract
BACKGROUND: Christmas and New Year's holidays are risk factors for hospitalization, but the causes of this "holiday effect" are uncertain. In particular, clinical complexity (CC) has never been assessed in this setting. We therefore sought to determine whether patients admitted to the hospital during the December holiday period had greater CC compared to those admitted during a contiguous non-holiday period. METHODS: This is a prospective, longitudinal study conducted in an academic ward of internal medicine in 2017-2019. Overall, 227 consecutive adult patients were enrolled, including 106 cases (mean age 79.4±12.8 years, 55 females; 15 December-15 January) and 121 controls (mean age 74.3±16.6 years, 56 females; 16 January-16 February). Demographic characteristics, CC, length of stay, and early mortality rate were assessed. Logistic regression analyses for the evaluation of independent correlates of being a holiday case were computed. RESULTS: Cases displayed greater CC (17.7±5.5 vs 15.2±5.9; p = 0.001), with greater impact of socioeconomic (3.51±1.7 vs 2.9±1.7; p = 0.012) and behavioral (2.36±1.6 vs 1.9±1.8; p = 0.01) CC components. Cases were also significantly frailer according to the Edmonton Frail Scale (8.0±2.8 vs 6.4±3.1; p<0.001), whilst having similar disease burden, as measured by the CIRS comorbidity index. Age (OR 1.02; p = 0.039), low income (OR 1.97, 95% CI 1.10-3.55; p = 0.023), and total CC (OR 1.06; p = 0.014) independently correlated with the cases. Also, cases showed a longer length of stay (median 15.5 vs 11 days; p = 0.0016) and higher in-hospital (12 vs 4 events; p = 0.021) and 30-day (14 vs 6 events; p = 0.035) mortality. CONCLUSIONS: Patients hospitalized during the December holiday period had worse health outcomes, and this could be attributable to the grater CC, especially related to socioeconomic (social deprivation, low income) and behavioral factors (inappropriate diet). The evaluation of all CC components could potentially represent a useful tool for a more rational resource allocation over this time of the year.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".