Reason analysis and management countermeasures of surgical outpatient hangs the wrong number phenomenon
Bibliographic record
Abstract
Objective: To analyze the reasons and categories of wrong registration of outpatients,recommend rectification measures,reduce the incidence of wrong registration,thus shorten the treatment time of patients.Methods: Sample survey method combined with questionnaire survey was adopted in this article,the number of patients registered of outpatient surgery in the second quarter from April to June in 2010 was taken as the statistical sample,the categories and reasons of wrong registration were summarized.Results: The main reasons for the wrong registration of outpatients include patient's own factors,factors of registered staff and some other indirect factors.In view of these reasons,corresponding rectification measures were recommended.Conclusion: By the measures of improving propaganda of popularizing medical knowledge for patients,raising the medical level of registered staff and optimizing registered process,the percentage of wrong registration of outpatient surgery in 2011 decreased to 0.83% which declined by 84 percent compared with the same period in 2010.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".