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Record W3103057671 · doi:10.4103/gjtm.gjtm_80_20

Paid plasma in low- and middle-income countries: The strategy or the strategy-frustrating: A short account of Iran experience involved

2020· article· en· W3103057671 on OpenAlexaboutno aff
Mahmoud Hadipour Dehshal

Bibliographic record

VenueGlobal Journal of Transfusion Medicine · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsnot available
Fundersnot available
KeywordsDonationBusinessPaymentBlood collectionTurnoverProfit (economics)MedicineEconomicsEconomic growthFinanceEmergency medicineMicroeconomicsManagement

Abstract

fetched live from OpenAlex

Sir, WHO and International Society of Blood Transfusion have emphasized on voluntary donation of blood, plasma, stem cell, and transplant. The proponents of the paid system hold the contrary belief that the voluntary unpaid plasma donations would not be adequate enough to meet the demand for plasma derived medicinal products (PDMPs) thereby the patients would be deprived from access to the essential pharmaceuticals.[1] However, the opponents of the paid plasma system assert that it would help the spread of newly emerging diseases in the society.[2] Canadian Blood Services has stated that paid plasma collection has not helped the supply of PDMPs; thus, the concept of for-profit plasma collection centers is illogical and contrary to voluntary system.[3] The United States experience shows that voluntary donation among 17–35-year-olds has largely shrunk and access to paid plasma donation could possibly worsen it. This experience shows that the concern for the negative effect of paid plasma on voluntary donation of blood, platelet, and plasma is not baseless. It is also claimed that payment to plasma donors does not just compensate for cost of donor transfer to and back from plasma centers but it takes the form of monetary aid to low income donors and is a clear practice of plasma purchase. To prove this claim, the proponents refer to the disproportional scatter of plasma collection centers and their being located on colleges, weak economic regions, borderlines, and previous factory hubs.[4] The concentration of 80% of plasma centers of the states adjacent to the most vulnerable regions shows that pharmaceutical companies collecting plasma aim at low-income and poor people who are interested to convert their plasma into cash.[5] The author believes that countries should consider three important factors. The first factor is the existence of an industry-scale capacity to manufacture PDMPs. The second factor pertains to the status of the social capital in each country and the consequent anticipation of the impact paid donation would place on the voluntary-based system of plasma and blood donation. The third factor is the estimation of the real demand for PDMPs in each country. We should not ignore the higher risks on the part of paid plasma donors than voluntary ones and we should try to strengthen the voluntary system. We have had no emerging diseases like AIDS in the recent years and ignoring the safest way might endanger our supplies in case of unknown emerging infections.[6] If a country can meet its plasma demands through voluntary nonremunerated donation, why should it risk by applying the paid plasma system and depart from the safe approach? In addition, concerns about mismatch between demand and supply may be addressed by offering nonmonetary incentives, compatible with social norms and standards. This will help achieve balance between the quality and quantity of plasma and avoid the negative effects of paid plasma collection on the nonremunerated voluntary system. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.059
GPT teacher head0.292
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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