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Record W2110254833 · doi:10.1093/ejcts/ezt539

Finding the optimal balance between extending limits and achieving outcomes

2013· letter· en· W2110254833 on OpenAlexaboutno aff
Stephen D. Cassivi

Bibliographic record

VenueEuropean Journal of Cardio-Thoracic Surgery · 2013
Typeletter
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsBalance (ability)Computer scienceMedicinePhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

It has always been the goal of pioneers and innovators to extend the limits of what is currently thought to be possible. This is as true in the field of thoracic surgery as in any field of endeavour. In lung transplantation, it was not long after the initial clinical successes of the University of Toronto group in the 1980s that they and others began to explore possible innovations that would allow more patients to benefit from this ground-breaking procedure [1]. Over the ensuing 30 years, during this first phase of the history of clinical lung transplantation, many research avenues have been explored in this goal to extend the opportunity of lung transplantation to a greater number of patients. These have included improved preservation techniques and solutions, advances in critical care and postoperative management and refinements in immunosuppression strategies [2–4]. The article by Moreno et al. [5], examines the interface between two important areas for extending the reach of lung transplantation: the utilization of ‘extended’ or ‘marginal’ donor lungs and the broadening of the range of severity of recipient candidates for lung transplantation. Extending the limits of what is considered an acceptable donor lung has been a recurring theme in the on-going efforts to overcome the paucity of donor lungs available for transplantation—So much so that so-called ‘extended’ donors have become almost the norm. Indeed, in the article by Moreno, 37% of donors were classified as ‘extended’. Equally, as experience increases, our collective limits on the severity of potential recipients have also expanded [6, 7]. This has been fuelled as well by donor organ allocation systems, such as the UNOS Lung Allocation Score system in the USA, which distribute organ offers based solely on the severity of illness of potential recipients [8]. The counterbalance, of course, to the extension of clinical boundaries must always be the resultant patient outcomes. This emphasis on appropriate outcomes becomes all the more germane in a discipline with obvious requirements for strict stewardship of rare but shared resources and a relatively high cost of care. Unjustifiably poor results do not only affect the recipient. The standard in transplantation is, therefore, understandably higher. It is for this reason that the research by Moreno et al. is an important effort from an experienced and well-respected lung transplantation programme. In their simplified but clever approach, Moreno et al. have tried to parse out the intersection between extending the limits of both donors and recipients. The endpoints analysed of 30-day mortality, primary graft dysfunction, onset of bronchiolitis obliterans and long-term survival are well chosen as they not only have widely accepted definitions but also are clearly the most relevant. After considering a number of caveats that the researchers themselves recognize and appropriately discuss, the main finding in the study is that the effect of extending the usual clinical boundaries is more hazardous with the extended recipient when compared with the extended donor. Median long-term survival of extended recipients was markedly lower than optimal recipients irrespective of the quality of the donor. Conversely, in the comparison with donors this was not observed; there was no overall difference in long-term survival according to donor quality. This is an important and novel observation as we continue to ‘push the envelope’ in all possible directions. The knowledge that the risks may be higher when extending the recipient criteria should help to guide us on a more secure path as we continue to extend the clinical limits of lung transplantation while trying to maintain acceptable and respectable outcomes. Naturally, there will be some that say that these findings were predictable. Indeed, this may be the sense of many who have been deeply invested in lung transplantation over the past three decades. These are the experts that have the benefit of experience permitting them to successfully prove Malcolm Gladwell's thesis in Blink that an extensive past experience can allow good judgement, in the absence of hard data and seemingly based solely on intuition [9]. What Moreno's group provides is the first body of data to support these ‘intuitions’ that recipient severity has a greater impact on outcomes than donor quality. As we enter the next era in lung transplantation, we should endeavour to provide more data-driven refinements to both donor and recipient selection. This will allow us to better define this important interface between the donor and recipient. Moreno's work makes the correct inference that these determinations can and should be coupled together in each transplant event. On the one hand, identifying recipients who can afford to be transplanted with a ‘specifically extended’ donor while maintaining acceptable outcomes may provide the latitude necessary to allow other more ‘extended’ recipients to be earmarked for a more strictly defined ‘optimal donor’. This Benthamesque utilitarian ideal of achieving the greater good for the greater number of patients will be possible only with reliable data from further research in the direction initiated by this paper.

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)0.011
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.040
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0070.009
Open science0.0020.004
Research integrity0.0110.018
Insufficient payload (model declined to judge)0.0060.003

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 head0.080
GPT teacher head0.336
Teacher spread0.256 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2013
Admission routes1
Has abstractyes

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Same venueEuropean Journal of Cardio-Thoracic SurgerySame topicTransplantation: Methods and OutcomesFrench-language works237,207