Bibliographic record
Abstract
Thank you for an opportunity to respond to the letter by Professors Prielipp and Butterworth referring to Table 5 in our recent article (1). In their letter, the authors caution the readers of Anesthesia & Analgesia that our proposed guidelines for perioperative management of patients with sickle cell disorders undergoing coronary artery bypass graft (CABG) surgery (1) should not be regarded as clinical guidelines because they have not undergone the customary scrutinized evaluation and confirmation in a prospective, randomized manner. We very much appreciate the authors comments and share their concerns that the standards of practice of medicine and development of clinical guidelines and protocols should be derived from the evidence-based delivery of health care. There is no doubt that evidence-based medicine is becoming a major part of our routine clinical practice; however, hard evidence to either support or refute the treatment of patients with sickle cell hemoglobinopathies undergoing cardiac surgery is simply not available. We emphasized in our paper that the current published literature on the above subject is limited to either single or serial case report studies, and most of them are confined to children undergoing correction of congenital heart defects. Where do we go from here? We conducted this cohort retrospective study to elicit if fast-track anesthesia (FTA) can be safely applied to patients with sickle cell trait undergoing CABG surgery. The results of our study confirmed that this subgroup of patients can be managed safely with the application of FTA protocol. We accept the criticism that our study was not a prospective, randomized, controlled trial (RCT); however, even the most rigorous RCT may raise the issue of its internal and external validity with potential problems of reproducibility. There is a huge variability in the acceptance and application of clinical guidelines even with less presumptuous topics between different countries, states, and institutions; furthermore, what seems to be an accepted and indisputable technique today may become an inappropriate or obscure practice tomorrow. The other difficulty is to apply the results from the study groups of patients to the individual situation. It is clear that clinical guidelines will always require modification in the light of the clinician’s experience and the needs of each patient. We consolidated all the up-to-date information and presented it as short table of reference to guide anesthesiologists who encounter this specific problem. Our recommendations were based on currently available evidence of safe and nonambiguous practice and our local expertise and should be used as an aid to clinical decision making. It is evident that the clinicians should always use their professional judgement to apply these guidelines to the individual. Whether we await the multicenter, adequately powered, prospective, randomized clinical trials which may or may not test each of our proposed recommendations or offer the practicing anesthesiologists a guide for the management of sickle cell patients undergoing cardiac surgery today, is a matter of conjecture. We would like to leave it to the readers’ discretion. George N. Djaiani MD, DEAA, FRCA Davy C. H. Cheng MD, FRCPC
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.244 | 0.167 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".