Bibliographic record
Abstract
[Objective]To understand seasonal variation regularity of outpatient amount,so as to provide references for the relevant administrative departments in the hospital staffing,consulting rooms and property arrangements.[Methods]Seasonal indexes were used to analyze the variation of outpatient amount based on the data from statistical report forms of the hospital. SPSS13.0 software was used for data analysis.[Results]There was periodicity and regularity about outpatient amount. The low-time each year of outpatient amount appeared in January and February. At the same time the crest-time appeared in July and August. The season index in August was the highest one in each month(110.75%)and in February was the lowest one(82.60%). The outpatient amount in third quarter was the highest one(342,300 person times)during the past nine years and in first quarter was the lowest one(284,600 person times). The season index in third quarter was the highest one in each quarter(107.82%)and in the first quarter was the lowest one(89.64%).[Conclusion]The regularity of seasonal variation of outpatient amount was useful to rational personnel and facilities allocation. We should increase staffing and facilities during the crest-time of outpatient amount. Meanwhile,we should arrange staffing to study and the exchange during the low-time for the human resources reserve and better services for patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".