Application of Semi-Markov Process For Model Incremental Change in HIV Staging with Cost Effect
Bibliographic record
Abstract
In the recent past, both non-parametric and parametric approaches have consistently been used to model cost effectiveness in a variety of health applications. This study applies the semi-Markov model while presenting the sojourn time with well-defined probability distributions. We employed the Weibull distribution to model the hazard function for each of the defined transition paths. We defined three distinct states of the semi-Markov process using the quantity of HIV virus in the blood of an HIV-infected person i.e., viral load (VL) copies in a milliliter (copies/mL). The three states were defined; VL < 200 copies/mL, 200 copies/mL < VL < 1,000 copies/mL, VL > 1,000 copies/mL and an absorbing state which is naturally death. We also developed a cumulative cost function, purposely to determine the average estimated cost per patient in each of the defined states. Incremental Cost Effectiveness Ratio (ICER) was utilized in the analysis of cost-effectiveness while comparing two program strategies i.e., Patients under the differentiated care model (DCM) and those who are not considered to be in any model of differentiated care during their respective ongoing clinical follow up. Results show the mean cost of the patients for each state 1, 2, and 3 was $765, $ 829, and $ 1,395 respectively. More so, the computed ICER ratio was $ 484/life-year-saved. In conclusion, the cost of keeping patients in state 1 (on DCM) was relatively cheaper and more efficient compared to the other states.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".