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Record W3097226354 · doi:10.1182/blood-2020-138917

Preparing for Platelet Shortages: Which Surgeries Should be Cancelled?

2020· article· en· W3097226354 on OpenAlexaffabout
Catherine Dubé, Christine Cserti‐Gazdewich, Gord Tait, Jacob Pendergrast

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsToronto General HospitalUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsEconomic shortageMedicineBlood productPlatelet transfusionCardiopulmonary bypassPlateletEmergency medicineSurgeryMedical emergencyIntensive care medicineAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Due to their short shelf-life, platelet concentrates are particularly susceptible to the product shortages which may result from shortfalls in donor collections or disruptions to the manufacturing and supply chain. Mechanisms to address shortages are particularly important in light of the ongoing COVID-19 pandemic. Canadian Blood Services (CBS), the national blood center serving Canada except Quebec, called a national Green Advisory Phase from May to June 2020 on platelet products and O negative red blood cells, requesting daily hospital inventory reports. When such shortages occur, it may be challenging to identify which surgical procedures are likely to require platelet transfusion support and should therefore be rescheduled. Methods: Information systems maintained by the Blood Transfusion Service (Wellsky®) and the Department of Surgical Services (ORSOS®) were cross-referenced for the 2019 calendar year at a large adult teaching hospital in Toronto with active cardiovascular and transplant programs. Only procedures that were performed more than 25 times during this period were included in the analysis. Average platelet consumption on the day of surgery, 1 week post-operatively and 30 days post-operatively was calculated. Results: 50 procedures with a potential for requiring intra-operative platelet transfusion support were identified, with the greatest demand being those involving cardiopulmonary bypass (CBP) support (27% of cases transfused, median of 2.7 units per case) and heart, liver and lung transplantation (24% of cases transfused, median of 4.2 units per case). Although spinal surgery as a group was not a high platelet consumer (4% of cases transfused, median of 0.3 units per case), certain complex procedures such as thoracic-lumbar laminectomy and thoracic-lumbar decompression and fusion were more at risk of requiring platelet support during and in the post-operative period. Due to their frequency, procedures on CPB created the highest demand in platelets with close to 4500 units distributed during the year. Standard deviations for many procedures was large, up to 10 units in the intra-operative period. Other procedures with low-risk of intra-operative platelet transfusion support but a possibility of requiring platelet transfusion in the 30-day post-operative period included renal transplantation and neurosurgical procedures. Interestingly, renal transplant patients generally did not require transfusion support during surgery or in the immediate post-operative period but only when looking at 30-day requirements. For most patients, these procedures would therefore be at risk only in the setting of very prolonged shortages. Conclusion: Data-driven demand forecasting for intra-operative and post-operative platelet transfusion support may be of value in risk-benefit analysis of proceeding with specific surgical procedures in the setting of platelet shortages. However, some of the procedures with the highest platelet consumption serve the sickest patients and cannot be postponed. Disclosures No relevant conflicts of interest to declare.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.054
GPT teacher head0.266
Teacher spread0.212 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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