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A review of nearly two decades in an autologous blood programme: the rise and fall of activity

2006· review· en· W2143416893 on OpenAlexaffabout
G. Rock, Rolf F. M. Berger, Janis Bormanis, Antonio Giulivi, Susie ElSaadany, Muhammad Afzal, John D. Beard, Doris Neurath, Tawanna Jones

Bibliographic record

VenueTransfusion Medicine · 2006
Typereview
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsOttawa HospitalHealth CanadaUniversity of Ottawa
Fundersnot available
KeywordsMedicineDonationAutologous bloodBlood donorBlood transfusionSurgeryPediatricsEmergency medicineImmunology

Abstract

fetched live from OpenAlex

Autologous blood donation (ABD) has been widely recommended. Data from one of the oldest hospital-based programmes in Canada describe both activities and drawbacks. Data were compared over the nearly two decades of activity that peaked in 1996. A 5-year review of recent activity showed that of the 2410 patients referred for consideration, 1823 (75.64%) were accepted into the programme. Surgical services requested 5825 units of autologous blood. Of these, 3147 units were donated by 1536 patients, 803 units were transfused in the operating room and 558 units were given postoperatively. In total, only 1361 units (43.25%) were transfused. The mean age of the patients was 58 years (median 61 years and mode 69 years). The haemoglobin concentrations before donation were significantly higher, averaging 145.2 g L(-1) before donation and 114.9 g L(-1) immediately before surgery, whereas at the time of discharge, the haemoglobin concentration averaged 126.2 g L(-1) (P = 0.0001) in transfused patients. Data from this well-established ABD programme indicate less than 50% overall utilization. The activity in the programme increased until 1996 following which it dropped progressively. The low haemoglobin concentration after surgery is of concern and should foster a transfusion algorithm for these patients.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.800
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.057
GPT teacher head0.379
Teacher spread0.322 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations19
Published2006
Admission routes2
Has abstractyes

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