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Record W2416320090

Transfusion as a predictor of recurrence and survival in head and neck cancer surgery patients.

2010· article· en· W2416320090 on OpenAlexaff
Jason Chau, Hadi Seikaly

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineGynecologyHead and neckSurgery
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: There is mixed evidence regarding the association of perioperative blood transfusion to disease recurrence and mortality in head and neck cancer patients. Moreover, few investigations have examined the effects of leukocyte-depleted (leukodepleted) red cell transfusion. The presented study was undertaken to ascertain whether perioperative transfusion of leukodepleted blood is associated with recurrence and survival in head and neck cancer surgery patients. METHODS: The records of all patients having undergone head and neck surgery for malignant disease between October 1996 and October 2002 were reviewed. Hospital, blood bank, and cancer registry database records were reviewed and data were recorded onto a standardized computer spreadsheet. The primary outcome variable was the number of perioperatively transfused units of allogeneic leukodepleted blood. Multivariate analysis and Cox regression methods were employed. RESULTS: Five hundred twenty patients met the criteria for inclusion in the study. Recurrence and mortality rates were significantly different between transfusion and no-transfusion groups, in favour of the no-transfusion group. In addition to age, T stage, and N stage, multivariate analysis revealed leukodepleted blood transfusion to be an independent predictor of both recurrence (odds ratio 1.6) and survival (hazard ratio 1.5). CONCLUSION: Perioperative transfusion of leukodepleted blood is associated with higher recurrence rates and decreased survival in head and neck cancer surgery.

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)0.000
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 score0.266
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.242
Teacher spread0.223 · 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

Citations45
Published2010
Admission routes1
Has abstractyes

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