Temperature Data Analysis of Diphtheria-Tetanus-Pertussis and Hepatitis B Vaccine Cold Chain Monitoring in Northern Part of Thailand
Bibliographic record
Abstract
Background Information: Vaccines are temperature-sensitive biological preparations, the proper range were cold chain period or 2-8 °C. The change of the temperature or cold chain breakdown might be effect to vaccines quality assurance.Aim: The temperature data of Diphtheria-Tetanus-Pertussis and Hepatitis B vaccine, DPT- HB vaccine in fiscal year 2011-2012 were analyzed and aimed to find the factor that effected to vaccine cold chain system.Method: Temperature data from health care units in 8 provinces collected by computerized data logger were analyzed by SPSS for window version 17.0 and Logtag analyzer program.Result: From 322 health care units, most of data was reported from Chiang Rai province and collected from Primary Care Unit; PCU in both fiscal year. The period of time for recording of the data logger were very fluctuates. The highest number of data was reported in October, 2011 and in February, 2012. DPT-HB vaccine temperature had lower than 2°C at 86.9%, higher than 8°C at 90.4% in 2011, and lower than 2°C at 78.5%, higher than 8°C at 92.5% in 2012. Type of health care unit did not effect to vaccine’s temperature monitor but seasonal had significant effect.Conclusion: Type of health care unit did not effect to vaccine’s temperature control. Seasonal had significant effect to vaccine’s temperature control. Based on the study results, adequate equipment, provide training and supervision about new and current computerize data logger were recommended to support to maximize the efficacy and effectiveness of vaccine and cold chain monitoring in health care unit.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".