CONSEQUENCES OF REFFERALS FROM A SECONDARY HEALTH CARE CENTER
Bibliographic record
Abstract
Objective: The main objective of this research work is to evaluate the recent referral nature from an institute with secondary health care facility and to determine what the consequences were in real condition to the patients. These facilities play very important part in the system of health care especially for the people from remote areas or undeveloped areas of city. Methodology: Hospital of Lal Quarter Samanabad, Lahore was the venue of this research work. This research work was retroactive as well as retrospective in nature. The study of total 50 referrals carried out retroactively and study of fifty referrals carried out retrospectively from March 2018 to June 2018. We obtained the various indications for the referrals from the record of referrals for patients; record was under the custody of institute. The analysis of collected information carried out with SPSS V.10. Results: In current research work about 86.0% patients recovered after treatment at the referral health care centers whereas 12 patients did not go to the referral centers after achievement of relief from received medicines from referring center. Two patients died, one because of uncontrollable diabetes mellitus and second died because of myocardial infarction. Conclusion: Ischemic heart diseases in these areas require particular attention. There is also vital requirement to build the tertiary care center adjacent to those referring centers to handle the cases with extreme emergency condition. KEY WORDS: Secondary, tertiary, referring, ischemic, disease, diabetes mellitus, SPSS, methodology.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".