Surgical incisions in sea turtles have a moderately high complication occurrence but heal well with appropriate management: 70 incisions for 52 turtles (2008–2024)
Bibliographic record
Abstract
Objective: To document surgical incision healing for hospitalized sea turtles. Methods: Medical records for sea turtle surgical cases from a single hospital between 2008 and 2024 were retrospectively evaluated. Cases that involved traumatic wounds or open fractures that were present at the time of admission, ophthalmic surgery, or acoustic transmitter implantations were excluded. Species, weight, time since admission to the hospital, surgical procedure, methods, duration, temporal proximity of diagnostic testing and antibiotic use, and presence of major comorbidities were evaluated as related to healing duration and complication occurrence. Complicated incisions were defined as those that required substantial additional medical or surgical management to promote healing. Results: 48 Kemp's ridley turtles (Lepidochelys kempii) and 4 loggerhead turtles (Caretta caretta) had 70 surgical incisions. Fifty-five incisions for 38 turtles healed, while 14 turtles (15 incisions) died prior to healing. Complications occurred in 20% of the 55 incisions. Orthopedic surgical incisions had a higher complication rate (37.5%) compared to soft tissue surgical incisions (6.5%). Complicated incisions had longer healing duration (mean ± SD, 74 ± 25 days; median, 75 days; range, 40 to 113 days) compared to uneventful incisions (mean ± SD, 41 ± 13 days; median, 39 days; range, 20 to 81 days). Body weight was positively associated with healing duration. Although the use of suture instead of staples for skin closure was positively associated with complications, this may have been related to nature of the surgery (orthopedic vs soft tissue) rather than the skin closure method. Conclusions: Sea turtle surgical incisions have a moderately high rate of complications, especially for orthopedic procedures. Complications may not be apparent for several weeks, but they can be successfully managed. Incisions generally heal within several months, even if complicated. Clinical Relevance: Given the complication rate in the present study, careful postoperative monitoring is recommended for several months following surgery in sea turtles, and additional management may be needed in cases with complications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".