Availability and consumption status of CFC and non-CFC inhalers for asthma and chronic obstructive pulmonary diseases in Thailand.
Bibliographic record
Abstract
In response to the Montreal Protocol and the calls for global early-bird CFC phase-out before 2010, the demand and supply status of both CFC and non-CFC inhalers for prevention and treatment of asthma and COPD in Thailand were evaluated to determine how soon the country would be able to discontinue CFC MDIs with least impacts to both consumers and importers. Availability and supply of the inhalers were collected from registration and importation database of the Thai FDA. Demand and product cost were obtained from the local importers and from IMS, Thailand. Available inhaled products comprise of 39% CFC MDIs, 28% DPIs, 20% solutions for nebulizers and 13% HFA MDls, respectively. All 31 brands of portable hand-held inhalers, comprising 16 CFC MDIs, 6 HFA MDIs and 9 DPIs, are imported, only solutions for nebulization are locally manufactured. Salbutamol is mostly prescribed MDI, its consumption is over 50% of all. The transition to non-CFC alternatives (HFA MDIs and DPIs) has become evidence since 2000. After being informed about the demand and supply of the inhalers, in 2005, Thai FDA has announced its CFC phase-out policy and encouraged importation of HFA alternatives by facilitating the registration and approval process. When the most prescribing CFC MDls, salbutamol, is completely replaced with non-CFC form in 2006, Thailand would be able to reduce considerable amount of CFCs into our atmosphere.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".