Bibliographic record
Abstract
OBJECTIVE: We hypothesized that the rate of nonelective hospital admissions for diverticulitis conforms to seasonal variation. DESIGN: Retrospective cohort analysis. SETTING: Patients admitted to hospitals in the Nationwide Inpatient Sample, a 20% sample of US community hospitals. PATIENTS: We identified patients with a nonelective admission or discharge for diverticulitis from January 1, 1997, through December 31, 2005, and determined the proportion of diverticulitis admissions (standardized to all inpatient admissions) for a particular admission month or discharge quarter. Next, we analyzed the potential effects of region, age, sex, and race on excess seasonal admissions for diverticulitis. RESULTS: On average, total nonelective admissions for diverticulitis were lowest in February (23 744 admissions) and highest in August (29 733 admissions), a 25.2% increase in cases. Similarly, diverticulitis discharges increased by 14.3% during the third quarter compared with the first (P < .001). A significant seasonal pattern of diverticulitis admissions was identified that conformed to a major sinusoidal component (P < .001). The excess seasonal burden of nonelective diverticulitis admissions in the third quarter was noted across US census regions, age, sex, and race. CONCLUSIONS: Hospitalization for diverticulitis adheres to a sinusoidal pattern, with more nonelective admissions occurring during the summer months. The excess summer burden of diverticulitis is noted across US census regions, age, sex, and race. A more thorough understanding of these trends may provide a mechanism to identify a potential trigger for diverticulitis.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".