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Record W4206656183 · doi:10.1093/asj/sjab401

The Emperor Has No Platelets: Minimal Effects in an Alopecia Split-Scalp Study Unsurprising as Platelet-Rich Plasma Was Actually Platelet-Poor

2021· article· en· W4206656183 on OpenAlexaff
Patrick K Yam

Bibliographic record

VenueAesthetic Surgery Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicPeriodontal Regeneration and Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicinePlateletScalpPlatelet-rich plasmaEmperorSurgeryDermatologyInternal medicine

Abstract

fetched live from OpenAlex

A recent paper by Dr Gordon Sasaki looked at the effect on alopecia of 2 different platelet concentrations of platelet-rich plasma (PRP) compared to placebo.1 Although improvement was associated with the higher concentration, statistical significance was not reached. As the main variable, and the basis of this study, platelet concentration deserves a closer look. This concentration may be expressed as the platelet increase factor (PIF): PIF = (PRP platelet concentration)/(whole blood platelet concentration). The PIF was reported as 4.5× in this study, obtained from the datasheet provided by the manufacturer of the PRP kits used in Dr Sasaki's study, Eclipse (The Colony, TX), who also sponsored the study. This value is foundational to any conclusions reached. A PIF of 4.5× would equal 1 to 1.5 million platelets/µL, which Dr Sasaki notes is widely believed to be the optimal concentration for favorable results. Indeed, the classic definition of PRP is a minimum of 1 million platelets/µL; conversely, concentrations below whole blood (~200,000 platelets/µL) are termed platelet-poor plasma (PPP).2 Presumably to verify the PRP platelet concentration, Dr Sasaki sent 1 mL from each PRP sample to a local hospital laboratory for Coulter Counter analysis (CCA). He writes, “quantification of platelets … by Coulter Counter in Batches A and B were calculated as 4.5-fold increases over baseline values.” However, a check of this calculation, ie the ratio of CCA PRP platelet concentration to baseline platelet concentrations, shows the PIF was actually only 0.1× to 0.2×, far lower than 4.5× (Table 1). In other words, PPP was used as treatment instead of PRP. Platelet Increase Factor based on Coulter Counter Analysis Values are mean [standard deviation] or number. CCA, Coulter Counter analysis; PIF, platelet increase factor; PRP, platelet-rich plasma. aValues from Table 5 in Sasaki.1 bPIF = (platelet concentration PRP)/(whole blood platelet concentration). Platelet Increase Factor based on Coulter Counter Analysis Values are mean [standard deviation] or number. CCA, Coulter Counter analysis; PIF, platelet increase factor; PRP, platelet-rich plasma. aValues from Table 5 in Sasaki.1 bPIF = (platelet concentration PRP)/(whole blood platelet concentration). For example, according to Table 5 in Dr Sasaki’s paper, CCA showed that the mean number of platelets for males, Batch A PRP (5 mL) was 136,991,250, which equates to 27,398/µL, or only 10% of the baseline platelet concentration of 276,750/µL. Therefore, the PIF is only 0.1× and 27,398/µL is 36.5× lower than the optimal value of 1 million/µL. This level of concentration would not qualify as PRP and it would not be surprising to see poor clinical results. Looking more closely at the study, 2 batches of PRP were used, A and B, representing low and high concentrations, respectively. However, both Batch A and B are described as “4.5 times the baseline platelet concentration of a patient’s whole blood.” Also, 1-mL aliquots of each batch were sent for CCA. Table 5 shows a very precise relationship between the batches; for both mean and standard deviations, the values for Batch B are exactly double the values for Batch A. This would not be expected if separate samples were measured by CCA. A PIF value of 4.5× still appears on Eclipse’s current website, based on “An average of several independent, verified tests” and “Whole Blood Platelets counts of 209 (106/mL)” (Table 2). Eclipse HC PRP3 PIF, platelet increase factor; PRP, platelet-rich plasma. The 44-mL kit consists of two 22-mL tubes.4 Eclipse HC PRP3 PIF, platelet increase factor; PRP, platelet-rich plasma. The 44-mL kit consists of two 22-mL tubes.4 Important questions arise from the manufacturer’s claims. How does the PIF improve from 3.5× to 4.5× simply by using 2 identical tubes, with no other change in protocol? How can the total number of platelets claimed be more than the starting number in whole blood multiplied by the claimed yield (~85%). For the 44-mL kit (2 × 22 mL): total platelets = 0.85 × (44 mL × 209 million/mL) = 7.8 billion < 10 billion claimed; for the 22-mL kit: total platelets = 0.85 × (22 mL × 209 million/mL) = 3.9 billion < 5 billion claimed. To obtain 5 and 10 billion platelets from the 22- and 44-mL kits would require yields of 93% and 123%, respectively. Where do the extra platelets come from? There is an extremely wide variety of PRP being produced by different systems available today. A recent comprehensive review of 34 different systems showed a 28× difference between the lowest and highest PIF, with platelet concentrations ranging from 79,000/µL (Eclipse) to 2.3 million/µL (Arthrex).5 The same review showed single-spin systems had an average PIF of 1.25×. Eclipse HC PRP consists of a single-spin system using a setting of 10 minutes × 1500g. Other researchers studying the effects of force and time on PRP preparation have concluded that maximum platelet yield is obtained at much lower settings,6-8 eg, 900g × 5 minutes7 or 160g × 10 minutes.7 As time and force increase, yield decreases, leading to a more “platelet pure” sample with fewer erythrocytes and leukocytes, but also lower platelet yield. These studies have demonstrated that a relatively high setting of 1500g × 10 minutes would lead to maximum volumes of plasma, but lower concentrations of all cell lines, including platelets, similar to the results of other independent studies of the Eclipse PRP system.9 Taking such a high value of 4.5× for PIF on a single-spin system, at face value, without independent verification, and contrary to other known research, can lead to unfounded conclusions. The only independent testing of concentration in this study, CCA, showed very low PIFs and platelet concentrations. It seems this study looked at the effects of PPP rather than PRP. Dr Yam has used PRP for regenerative and aesthetic purposes and has been an invited speaker on the preparation and use of PRP in clinical practice for international organizations (eg, IMCAS, FATS). The speaking engagements were unpaid, although one organization offered an honorarium after the fact that has not yet been received. Dr Yam has also used a hematology analyzer to test PRP and blood samples in clinical practice. The author has no financial relationships with any company related to PRP. The author received no financial support for the research, authorship, and publication of this article.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.028
GPT teacher head0.285
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainMethods
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".

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Citations2
Published2021
Admission routes1
Has abstractno

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