P.127 Management and outcome of spontaneous sub-aponeurotic fluid collections in infants: the Hospital for Sick Children experience and review of literature
Bibliographic record
Abstract
Background: Spontaneous sub-aponeurotic fluid collection (SSFC) is an uncommon and newly described entity of unknown etiology, observed in infants less than one year of age. In this paper, we report a series of 9 infants who presented to the Hospital for Sick Children with SSFC over the 2004 to 2015 period, focusing on the natural history of this rare condition. Methods: Data from the HSC was retrospectively reviewed. Patient age and gender, birth history, past medical history, laboratory findings, imaging characteristics, management, and outcome were analyzed. Results: Our case series consists of 4 males and 5 females, ranging from 5 weeks to 11 months of age. All cases of SSFC developed spontaneously over a period of days, and the infants had no history of injuries or hair manipulation. Six patients had a remote history of forceps or vacuum-assisted births. One patient experienced fluctuating fluid collection size over 4 months, but in all the cases, the collections resolved spontaneously without structural or infectious complications. Conclusions: This is the largest series describing SSFC to date, and summarizes the experience of a large academic neurosurgical center. SSFCs develop spontaneously without immediate preceding trauma, and an extensive hematology or child abuse workup is not necessary. A conservative approach with outpatient follow-up is advocated.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".