A retrospective study on the prevalence of hypothalamic-pituitary axis disturbances as of six months post Traumatic Brain Injury in patients hospitalized in intensive functional rehabilitation
Bibliographic record
Abstract
Abstract Objectives: This retrospective study aims to estimate the prevalence of neuroendocrine disturbances as early as six months after traumatic brain injury (TBI) in patients hospitalized in intensive functional rehabilitation (IFR).Methods: Retrospective study of all patients aged 17 years or older admitted to an IFR between 2014-2016 with a diagnosis of mild-complex to severe TBI. Charts were reviewed (N=300) and those with neuroendocrine lab results were retained (N=56). Pituitary-dependent hormone dosages were collected. Results: An estimated prevalence between 4.67 and 30.43% with at least one hormonal anomaly was noted. Compared to the literature, lower disturbances for all pituitary axes except central adrenal insufficiency were observed. The most significant prevalence in our cohort is of the adrenal axis estimated at 4 and 26.09%, while for thyroid, gonadal and growth hormone axes, a suspicion of disturbance was detected in a single patient per axis for an estimated prevalence of 0.33 to 2.17% each.Conclusion: Results did not show a very significant prevalence in chronic patients with TBI who were hospitalized in IFR. We believe that systematic testing is not necessary and propose workups only if symptomatic and after a comprehensive clinical history. Hypopituitarism is complex, and endocrinology consultation is recommended.
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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 | 0.001 |
| 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) | 0.002 | 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".