Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.008 | 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 it